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Article(id=1241035820535173541, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241035810628235909, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202408460, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1724601600000, receivedDateStr=2024-08-26, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773815596946, onlineDateStr=2026-03-18, pubDate=1733760000000, pubDateStr=2024-12-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773815596946, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773815596946, creator=13701087609, updateTime=1773815596946, updator=13701087609, issue=Issue{id=1241035810628235909, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='23', pageStart='4225', pageEnd='4416', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773815594584, creator=13701087609, updateTime=1773815743629, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241036435843764756, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241035810628235909, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241036435843764757, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241035810628235909, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=4231, endPage=4237, ext={EN=ArticleExt(id=1241035820992352709, articleId=1241035820535173541, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Study on the association between sleep regularity and central obesity in the population aged 40 to 65 years, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the association between the Sleep Regularity Index (SRI) and central obesity, as well as the differences among different age and gender groups, to provide a reference for research on sleep regularity and central obesity in the population.

Methods

A questionnaire survey and objective sleep monitoring were conducted among individuals aged 40 to 65 who underwent health check-ups at the First People’s Hospital of Fuquan city from March to November 2022, ultimately including 522 participants with complete data. The association between SRI and the risk of central obesity was assessed using a non-conditional binary logistic regression model and restricted cubic splines, along with subgroup analyses based on gender and age.

Results

Among the participants included in the study, 331 individuals (64.41%) were classified as having central obesity. After adjusting for confounding variables, the results from the restricted cubic splines indicated that when the SRI score exceeded 72.67, the risk of central obesity gradually decreased with increasing SRI scores. The non-conditional binary logistic regression model revealed that individuals with more regular sleep patterns had a lower risk of central obesity compared to those with severely irregular sleep patterns. Compared to the lowest SRI quartile (Q1), the odds ratios (OR) for Q3 and Q4 were 0.56(95%CI: 0.32-0.99) and 0.51 (95%CI: 0.29-0.90), respectively. Subgroup analyses showed that in male participants and those aged 50 and above, the risk of central obesity decreased with more regular sleep patterns. In males, compared to Q1, the OR for Q3 was 0.41 (95%CI: 0.19-0.91) and for Q4 was 0.37 (95%CI: 0.16-0.86). In the population aged ≥50, the OR for Q3 was 0.41(95%CI: 0.19-0.89) and for Q4 was 0.31 (95%CI: 0.14-0.71). Sensitivity analyses yielded consistent results.

Conclusion

There is a certain association between sleep regularity, as assessed by the SRI and central obesity. Higher SRI scores are associated with a lower risk of developing central obesity, particularly in males and individuals aged 50 and above.

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目的

探讨睡眠规律性指数（SRI）与中心性肥胖的关联以及不同年龄及性别人群的差异。为人群睡眠规律性与中心性肥胖的研究提供参考依据。

方法

对2022年3—11月在福泉市第一人民医院体检中心体检的40～65岁人群开展问卷调查及客观睡眠监测，最终纳入522名数据完整的调查对象。采用非条件二分类logistic回归模型以及限制性立方样条评估SRI与中心性肥胖患病风险的关联，并进行性别、年龄的亚组分析。

结果

本次纳入研究人群中，中心性肥胖人群共331人（64.41%）。在调整混杂变量后，限制性立方样条结果显示当SRI得分>72.67分时，随着SRI得分增加，中心性肥胖风险逐渐降低；非条件二分类logistic回归模型结果显示，与睡眠严重不规律人群相比，睡眠越规律的人群患中心性肥胖的风险越低，与SRI的Q1组相比，Q3组OR=0.56（95%CI：0.32～0.99），Q4组OR=0.51（95%CI：0.29～0.90）；亚组分析结果显示，在男性人群和≥50岁人群中睡眠越规律的人群患中心性肥胖的风险越低。与SRI的Q1组相比，在男性中，Q3组OR=0.41（95%CI：0.19～0.91），Q4组OR=0.37（95%CI：0.16～0.86）； 在≥50岁人群中，Q3组OR=0.41（95%CI：0.19～0.89），Q4组OR=0.31（95%CI：0.14～0.71）。敏感性分析结果基本保持一致。

结论

由SRI所评估的睡眠规律与中心性肥胖之间存在一定关联，SRI得分越高，其发生中心性肥胖的风险越低，尤其在男性以及≥50岁人群中。

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汪俊华，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=QCjUgQ3JTsBUQk43eOuJQg==, magXml=q2D8sHrsco693Ntb9/QJRw==, pdfUrl=null, pdf=bSi6BkFQBhE+ylLfQ5uT4w==, pdfFileSize=1049904, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=S+vTcIAYDJhbqVkaO+lEIA==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=TX+Zdj4cm0720SXa1dYgkQ==, mapNumber=null, authorCompany=null, fund=null, authors=

商元昊（2000—），男，硕士在读，研究方向：慢性病流行病学

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商元昊（2000—），男，硕士在读，研究方向：慢性病流行病学

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Basic characteristics of research participants ［M（P25，P75），，n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总人群(n=522)非中心肥胖 (n=191)中心性肥胖 (n=331)t/Z/χ2值P值
年龄（岁）51.00 (47.00, 57.00)49.00 (45.00, 55.00)52.00 (48.00, 57.00)-4.40b<.001
夜间睡眠时长(h)6.78±0.836.85±0.826.75±0.841.32a0.188
睡眠连续性得分57.29 (53.18, 61.86)58.00 (54.50, 62.29)56.86 (52.93, 61.43)1.94 b0.052
SRI72.59 (67.44, 78.61)75.65 (69.66, 80.78)71.41 (66.48, 76.82)-4.62 b<.001
体力活动水平(MET-h/d)15.36 (11.43, 20.57)15.50 (11.17, 20.51)15.25 (11.61, 20.74)-0.22 b0.826
文化程度6.99c0.030
初中及以下64 (12.26)15 (23.44)49 (76.56)
高中及中专72 (13.79)23 (31.94)49 (68.06)
大专及以上386 (73.95)153 (39.64)233 (60.36)
婚姻状况2.50 c0.114
已婚485 (92.91)173 (35.67)312 (64.33)
其他37 (7.09)18 (48.65)19 (51.35)
性别25.27 c<.001
女247 (47.32)118 (47.77)129 (52.23)
男275 (52.68)73 (26.55)202 (73.45)
吸烟7.38 c0.007
否376 (72.03)151 (40.16)225 (59.84)
是146 (27.97)40 (27.40)106 (72.60)
饮酒6.81 c0.009
否197 (37.74)86 (43.65)111 (56.35)
是325 (62.26)105 (32.31)220 (67.69)
工作状态0.38 c0.540
退休118 (22.61)46 (38.98)72 (61.02)
在职404 (77.39)145 (35.89)259 (64.11)
家庭收入（元/月）1.72 c0.190
≥10 000230 (44.06)77 (33.48)153 (66.52)
<10 000292 (55.94)114 (39.04)178 (60.96)
午睡习惯5.86 c0.053
从不48 (9.20)10 (20.83)38 (79.17)
偶尔102 (19.54)37 (36.27)65 (63.73)
经常372 (71.26)144 (38.71)228 (61.29)
打鼾22.36 c<.001
否238 (45.59)113 (47.48)125 (52.52)
是284 (54.41)78 (27.46)206 (72.54)
饮茶6.79 c0.009
否317 (60.73)130 (41.01)187 (58.99)
是205 (39.27)61 (29.76)144 (70.24)
咖啡0.01 c0.939
否506 (96.93)185 (36.56)321 (63.44)
是16 (3.07)6 (37.50)10 (62.50)
PSQI分组2.18 c0.139
≥7348 (66.67)135 (38.79)213 (61.21)
<7174 (33.33)56 (32.18)118 (67.82)
), ArticleFig(id=1241069120200962565, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035820535173541, language=CN, label=表1, caption=

一般特征分析表［M（P25，P75），，n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总人群(n=522)非中心肥胖 (n=191)中心性肥胖 (n=331)t/Z/χ2值P值
年龄（岁）51.00 (47.00, 57.00)49.00 (45.00, 55.00)52.00 (48.00, 57.00)-4.40b<.001
夜间睡眠时长(h)6.78±0.836.85±0.826.75±0.841.32a0.188
睡眠连续性得分57.29 (53.18, 61.86)58.00 (54.50, 62.29)56.86 (52.93, 61.43)1.94 b0.052
SRI72.59 (67.44, 78.61)75.65 (69.66, 80.78)71.41 (66.48, 76.82)-4.62 b<.001
体力活动水平(MET-h/d)15.36 (11.43, 20.57)15.50 (11.17, 20.51)15.25 (11.61, 20.74)-0.22 b0.826
文化程度6.99c0.030
初中及以下64 (12.26)15 (23.44)49 (76.56)
高中及中专72 (13.79)23 (31.94)49 (68.06)
大专及以上386 (73.95)153 (39.64)233 (60.36)
婚姻状况2.50 c0.114
已婚485 (92.91)173 (35.67)312 (64.33)
其他37 (7.09)18 (48.65)19 (51.35)
性别25.27 c<.001
女247 (47.32)118 (47.77)129 (52.23)
男275 (52.68)73 (26.55)202 (73.45)
吸烟7.38 c0.007
否376 (72.03)151 (40.16)225 (59.84)
是146 (27.97)40 (27.40)106 (72.60)
饮酒6.81 c0.009
否197 (37.74)86 (43.65)111 (56.35)
是325 (62.26)105 (32.31)220 (67.69)
工作状态0.38 c0.540
退休118 (22.61)46 (38.98)72 (61.02)
在职404 (77.39)145 (35.89)259 (64.11)
家庭收入（元/月）1.72 c0.190
≥10 000230 (44.06)77 (33.48)153 (66.52)
<10 000292 (55.94)114 (39.04)178 (60.96)
午睡习惯5.86 c0.053
从不48 (9.20)10 (20.83)38 (79.17)
偶尔102 (19.54)37 (36.27)65 (63.73)
经常372 (71.26)144 (38.71)228 (61.29)
打鼾22.36 c<.001
否238 (45.59)113 (47.48)125 (52.52)
是284 (54.41)78 (27.46)206 (72.54)
饮茶6.79 c0.009
否317 (60.73)130 (41.01)187 (58.99)
是205 (39.27)61 (29.76)144 (70.24)
咖啡0.01 c0.939
否506 (96.93)185 (36.56)321 (63.44)
是16 (3.07)6 (37.50)10 (62.50)
PSQI分组2.18 c0.139
≥7348 (66.67)135 (38.79)213 (61.21)
<7174 (33.33)56 (32.18)118 (67.82)
), ArticleFig(id=1241069120356151823, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035820535173541, language=EN, label=Table 2, caption=

Sleep characteristics under different sleep pattern groupings ［，M（P25，P75），n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
VariablesQ1 (n=131)Q2 (n=130)Q3 (n=130)Q4 (n=131)F/χ2值P值
夜间睡眠时长(h)6.53±0.896.72±0.776.82±0.827.05±0.779.27a<.001
白天小睡(h)1.00 (0.68, 1.32)0.88 (0.56, 1.15)0.67 (0.39, 0.92)0.54 (0.27, 0.83)75.49b<.001
睡眠连续性得分55.86 (52.64, 61.07)57.36 (53.00, 62.96)56.57 (52.75, 61.00)59.00 (55.86, 63.86)17.77 b<.001
午睡时长(min)42.86 (22.14, 62.14)42.86 (30.00, 64.29)42.86 (21.43, 60.00)40.00 (21.43, 60.00)4.30 b0.231
打鼾8.97c0.030
否49 (20.59)55 (23.11)62 (26.05)72 (30.25)
是82 (28.87)75 (26.41)68 (23.94)59 (20.77)
PSQI分组1.47 c0.690
≥784 (24.14)85 (24.43)92 (26.44)87 (25.00)
<747 (27.01)45 (25.86)38 (21.84)53 (25.00)
), ArticleFig(id=1241069120490369563, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035820535173541, language=CN, label=表2, caption=

不同睡眠规律分组下睡眠特征表 ［，M（P25，P75），n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
VariablesQ1 (n=131)Q2 (n=130)Q3 (n=130)Q4 (n=131)F/χ2值P值
夜间睡眠时长(h)6.53±0.896.72±0.776.82±0.827.05±0.779.27a<.001
白天小睡(h)1.00 (0.68, 1.32)0.88 (0.56, 1.15)0.67 (0.39, 0.92)0.54 (0.27, 0.83)75.49b<.001
睡眠连续性得分55.86 (52.64, 61.07)57.36 (53.00, 62.96)56.57 (52.75, 61.00)59.00 (55.86, 63.86)17.77 b<.001
午睡时长(min)42.86 (22.14, 62.14)42.86 (30.00, 64.29)42.86 (21.43, 60.00)40.00 (21.43, 60.00)4.30 b0.231
打鼾8.97c0.030
否49 (20.59)55 (23.11)62 (26.05)72 (30.25)
是82 (28.87)75 (26.41)68 (23.94)59 (20.77)
PSQI分组1.47 c0.690
≥784 (24.14)85 (24.43)92 (26.44)87 (25.00)
<747 (27.01)45 (25.86)38 (21.84)53 (25.00)
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40～65岁人群睡眠规律性与中心性肥胖的关联性研究
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商元昊 1 , 田祖航 1 , 谌世晖 2 , 谭存瑶 2 , 吴帮云 2 , 卯光艳 1 , 宋小玲 1 , 王子云 1 , 汪俊华 1
现代预防医学 | 流行病与统计方法 2024,51(23): 4231-4237
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现代预防医学 | 流行病与统计方法 2024, 51(23): 4231-4237
40～65岁人群睡眠规律性与中心性肥胖的关联性研究
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商元昊1, 田祖航1, 谌世晖2, 谭存瑶2, 吴帮云2, 卯光艳1, 宋小玲1, 王子云1, 汪俊华1
作者信息
  • 1.贵州医科大学公共卫生与健康学院，环境污染与疾病监控教育部重点实验室，贵州 贵安 561113
  • 2.贵州省黔南布依族苗族自治州福泉市疾病预防控制中心

    • 商元昊（2000—），男，硕士在读，研究方向：慢性病流行病学

通讯作者:

汪俊华，E-mail:
Study on the association between sleep regularity and central obesity in the population aged 40 to 65 years
Yuan-hao SHANG1, Zu-hang TIAN1, Shi-hui CHEN2, Cun-yao TAN2, Bang-yun WU2, Guang-yan MAO1, Xiao-ling SONG1, Zi-yun WANG1, Jun-hua WANG1
Affiliations
  • College of Public Health and Health, Guizhou Medical University, Key Laboratory of Environmental Pollution and Disease Monitoring, Ministry of Education, Gui’an New Area, Guizhou 561113, China
出版时间: 2024-12-10 doi: 10.20043/j.cnki.MPM.202408460
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摘要
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目的

探讨睡眠规律性指数（SRI）与中心性肥胖的关联以及不同年龄及性别人群的差异。为人群睡眠规律性与中心性肥胖的研究提供参考依据。

方法

对2022年3—11月在福泉市第一人民医院体检中心体检的40～65岁人群开展问卷调查及客观睡眠监测，最终纳入522名数据完整的调查对象。采用非条件二分类logistic回归模型以及限制性立方样条评估SRI与中心性肥胖患病风险的关联，并进行性别、年龄的亚组分析。

结果

本次纳入研究人群中，中心性肥胖人群共331人（64.41%）。在调整混杂变量后，限制性立方样条结果显示当SRI得分>72.67分时，随着SRI得分增加，中心性肥胖风险逐渐降低；非条件二分类logistic回归模型结果显示，与睡眠严重不规律人群相比，睡眠越规律的人群患中心性肥胖的风险越低，与SRI的Q1组相比，Q3组OR=0.56（95%CI：0.32～0.99），Q4组OR=0.51（95%CI：0.29～0.90）；亚组分析结果显示，在男性人群和≥50岁人群中睡眠越规律的人群患中心性肥胖的风险越低。与SRI的Q1组相比，在男性中，Q3组OR=0.41（95%CI：0.19～0.91），Q4组OR=0.37（95%CI：0.16～0.86）； 在≥50岁人群中，Q3组OR=0.41（95%CI：0.19～0.89），Q4组OR=0.31（95%CI：0.14～0.71）。敏感性分析结果基本保持一致。

结论

由SRI所评估的睡眠规律与中心性肥胖之间存在一定关联，SRI得分越高，其发生中心性肥胖的风险越低，尤其在男性以及≥50岁人群中。

睡眠规律性  /  中心性肥胖  /  SRI  /  昼夜节律  /  横断面研究
Objective

To explore the association between the Sleep Regularity Index (SRI) and central obesity, as well as the differences among different age and gender groups, to provide a reference for research on sleep regularity and central obesity in the population.

Methods

A questionnaire survey and objective sleep monitoring were conducted among individuals aged 40 to 65 who underwent health check-ups at the First People’s Hospital of Fuquan city from March to November 2022, ultimately including 522 participants with complete data. The association between SRI and the risk of central obesity was assessed using a non-conditional binary logistic regression model and restricted cubic splines, along with subgroup analyses based on gender and age.

Results

Among the participants included in the study, 331 individuals (64.41%) were classified as having central obesity. After adjusting for confounding variables, the results from the restricted cubic splines indicated that when the SRI score exceeded 72.67, the risk of central obesity gradually decreased with increasing SRI scores. The non-conditional binary logistic regression model revealed that individuals with more regular sleep patterns had a lower risk of central obesity compared to those with severely irregular sleep patterns. Compared to the lowest SRI quartile (Q1), the odds ratios (OR) for Q3 and Q4 were 0.56(95%CI: 0.32-0.99) and 0.51 (95%CI: 0.29-0.90), respectively. Subgroup analyses showed that in male participants and those aged 50 and above, the risk of central obesity decreased with more regular sleep patterns. In males, compared to Q1, the OR for Q3 was 0.41 (95%CI: 0.19-0.91) and for Q4 was 0.37 (95%CI: 0.16-0.86). In the population aged ≥50, the OR for Q3 was 0.41(95%CI: 0.19-0.89) and for Q4 was 0.31 (95%CI: 0.14-0.71). Sensitivity analyses yielded consistent results.

Conclusion

There is a certain association between sleep regularity, as assessed by the SRI and central obesity. Higher SRI scores are associated with a lower risk of developing central obesity, particularly in males and individuals aged 50 and above.

Sleep regularity  /  Central obesity  /  SRI  /  Circadian rhythm  /  Cross-sectional study
商元昊, 田祖航, 谌世晖, 谭存瑶, 吴帮云, 卯光艳, 宋小玲, 王子云, 汪俊华. 40～65岁人群睡眠规律性与中心性肥胖的关联性研究. 现代预防医学, 2024 , 51 (23) : 4231 -4237 . DOI: 10.20043/j.cnki.MPM.202408460
Yuan-hao SHANG, Zu-hang TIAN, Shi-hui CHEN, Cun-yao TAN, Bang-yun WU, Guang-yan MAO, Xiao-ling SONG, Zi-yun WANG, Jun-hua WANG. Study on the association between sleep regularity and central obesity in the population aged 40 to 65 years[J]. Modern Preventive Medicine, 2024 , 51 (23) : 4231 -4237 . DOI: 10.20043/j.cnki.MPM.202408460
正文
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肥胖被认为是各种代谢性疾病、心血管疾病的重要危险因素[1]。目前研究认为，与一般性肥胖相比，腹腔脂肪蓄积而形成的中心性肥胖可能与代谢紊乱以及心血管疾病发生风险关联更强[2-4]。因此，确定可改变的生活方式因素对于预防或减轻肥胖及相关疾病负担至关重要。睡眠被认为是影响个体健康的重要可改变生活方式之一，随着睡眠研究的进展，研究发现睡眠规律性以及昼夜节律对肥胖的发生过程中存在影响。有研究已经表明，较大的夜间睡眠时长变异与肥胖的发生有关[5-8]。但迄今为止使用的睡眠规律测量方法可能无法评估极度不规律的睡眠或捕捉睡眠时间的快速变化以及24h周期变化，如白天小睡、熬夜不睡等。而睡眠规律性指数（sleep regularity index, SRI）[9]作为量化睡眠时间快速变化的睡眠规律指标，目前已经有研究发现其与BMI以及心脏代谢风险等有关[10-13]，但与中心性肥胖之间的关联目前还尚缺乏证据。因此本研究旨在通过探讨SRI与中心性肥胖间的关联性，为完善对中心性肥胖的个体预防策略提供一定依据。
1 对象与方法
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1.1 研究对象
本研究数据来源于2022年3—11月“贵州省中老年人群睡眠特征及其与慢性病的随访研究”的基线调查，其对福泉市第一人民医院体检中心中符合项目纳排标准的对象进行调查，调查对象均签署知情同意。研究方案获贵州医科大学人体实验伦理委员会批准[2021（174）号、2022（7）号]。
纳入标准：（1）年龄在40～65岁者；（2）定期在福泉市第一人民医院体检中心体检；（3）对本研究知情同意，并签署知情同意书者。
排除标准：（1）患有严重心脑血管疾病，如冠心病（心肌梗死/心绞痛)、心房颤动、慢性心力衰竭、脑卒中（脑梗死）；（2）重大手术后，如脑部肿瘤切除、冠状动脉旁路移植术、器官移植术；（3）有明确职业病史或暴露史，或5年内从事过粉尘、噪声、焦炉等具有危害性因素工作；（4）收缩压≥180 mm Hg和（或）舒张压≥110 mm Hg；（5）无7 d完整睡眠数据者；（6）体检项目未涉及血脂检测。
2022年3—11月，累积完成基线问卷调查1 104人，发放手环1 050个，最终符合纳排标准，完成问卷调查并发放手环人数为1 043人。此次分析仅纳入2022年基线发放手环人数的睡眠信息进行分析，共522人具有连续7 d以上睡眠监测数据，最终本研究共纳入522名数据完整的调查对象进行数据分析。
1.2 研究方法
1.2.1 资料收集方法
调查包括问卷调查、客观睡眠监测、体格检查和实验室检测四部分。由统一培训的调查员以面对面询问的方式进行问卷调查，一般情况包括年龄、性别、文化程度、家庭收入、运动、疾病史等；通过告知参与者佩戴荣耀-5i智能手环，下载手机客户端（华为运动健康）并连接手环后进行日常活动，睡眠监测数据实时上传至APP。由专业人员操作并完成健康体检获得身高、体重、腰围、臀围、血压、血糖、血脂等信息。
1.2.2 相关定义以及判定标准
1.2.2.1 睡眠规律性指数（SRI）
结合心率指标及睡眠波型判断佩戴者是否处于正常睡眠状态，整理数据过程中考虑节假日的影响，均避开节假日期，且考虑教师人群工作特殊性，避开了教师寒暑假，通过截图方式收集各参与者自开始佩戴手环最近时间内连续7 d睡眠结构图，采用Python 3.9以及PyCharm 2023.1中EasyOCR包进行图片识别处理，提取夜间睡眠时长，入睡时间、醒来时间、白天小数时长、睡眠连续性得分、清醒次数等信息。
SRI是通过使用Python 开源包Sri（https://github.com/mengelhard/sri）[14]并采用Python 3.9以及PyCharm 2023.1进行计算，根据睡眠结构图从当天凌晨24点开始记录活动数据将24h内每个时期（间隔24 h，每天1 440个周期，一个时期为1 min）睡眠/觉醒阶段分别进行赋值，睡眠=0，清醒=1。SRI 分数范围为0～100，分数越低表示睡眠不规律，100表示睡眠完全规律。参考既往研究，将SRI按照四分位数（Q1、Q2、Q3、Q4）进行分类，其中最低的四分位数为睡眠严重不规律组，最高四分位数为睡眠最规律组。平均夜间睡眠时长定义为佩戴手环期间每晚睡眠时长的平均值。白天小睡、睡眠连续性得分以及清醒次数基于手环睡眠监测数据获得。
1.2.2.2 中心性肥胖
采用腰围/臀围（腰臀比）（waist-to-hip ratio，WHR）男性WHR≥0.9，女性WHR≥0.85定义为中心性肥胖；腰围（waist circumference，WC）男性WC≥90cm，女性≥85cm定义为中心性肥胖[15]。身高、体重使用SH-2009型身高体重仪(上禾科技有限公司)测量；使用皮尺水平环绕腹部，定位于受试者的髂嵴上缘与第十二肋下缘连线的中点水平以测量腰围；找出受试者臀围最大伸展度，将皮尺环绕臀部一周，将其贴住皮肤进行读数以测量臀围。测量结果以cm为单位，精确到0.1 cm。
1.2.2.3 相关协变量定义
相关的协变量通过面对面问卷调查和体检获得。包括性别、年龄、文化程度、经济收入、婚姻状况、吸烟、饮酒、身体活动、打鼾、工作状态、午睡习惯等相关变量。
（1）饮酒：指过去6个月内有饮用任何含乙醇成分饮料行为的人[16]；（2）吸烟：一生中连续或累积吸烟6个月及以上的人[17-18]；（3）家庭收入：通过询问家庭月收入而获得，并分成<1万和≥1万两组；（4）文化程度：通过询问：您的最高文化程度为？分为初中及以下、高中/中专/中技、大专/高技以及本科及以上；（5）身体活动：参照2024年体力活动指南，根据个体每天职业、交通、家务以及业余时间活动计算代谢当量总和（metabolic equivalents，METs）[19]；（6）打鼾：通过询问调查对象“是否有打鼾习惯”，分为“经常或偶尔”“否”两组；（7）工作状态：根据调查对象当前工作状态分为“在职”“退休”两组；（8）睡眠质量：采用匹兹堡睡眠质指数量表(Pittsburgh sleep quality index, PSQI)评估参与者的睡眠质量，总分范围0～21分，将PSQI得分>7分定义为睡眠质量差[20]。
1.2.3 统计分析
采用R 4.3.3版本进行统计分析；本研究采用WHR作为中心性肥胖指标进行分组。采用Shapiro-Wilk检验进行正态性检验，对于定量资料，服从正态分布的采用表示，不服从正态分布的采用［M（P25，P75）］表示，两组间比较采用t检验Mann-Whitney U检验，多组比较采用方差分析或Kruskal-waills H检验；定性资料采用率或构成比进行描述，组间比较使用χ2检验。
采用非条件二分类logistic回归模型计算比值比（OR）及95%置信区间（CI）评价SRI与中心性肥胖之间的关联性。以性别和年龄进行分层，进一步分析SRI与中心性肥胖在不同性别以及不同年龄人群中的差异。此外，使用限制性立方样条（restricted cubic spline，RCS）评估SRI与中心性肥胖间的剂量反应关系。此外，本次研究还使用WC作为中心性肥胖指标进行敏感性分析，并且由于结局发生率较大时，使用比值比（OR）可能会高估关联效应大小，因此本次研究进一步采用基于稳健误差方差的修正Poisson回归模型计算现患率比PR及95%CI来评估SRI与中心性肥胖之间的关联性以进行敏感性分析。利用DAGitty v3.0在线版本（http://www.dagitty.net/）绘制有向无环图（DAGS）确定睡眠与肥胖关联中所需调整的最小控制变量集，调整变量包括性别、年龄、文化程度、家庭收入、婚姻状况、吸烟、饮酒、身体活动、饮茶、咖啡、工作类型，检验水准α=0.05。
1.2.4 质量控制
为保证数据可靠性，问卷使用EpiData 3.1软件进行双人双录入，进行一致性检验。对于手环监测的睡眠信息，严格按照筛选规则筛选参与者调查日期最近的睡眠图；对于睡眠指标，将提取出来的数据以Excel表格保存，由双人进行核查。
2 结果
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2.1 一般特征分析
本次研究共纳入522人，男性275人（52.68%），女性247人（47.32），年龄为51.00（47.00, 57.00）岁。中心性肥胖人数共331人，占比64.41%。其中，年龄、性别、文化程度、吸烟、饮酒、SRI得分以及是否打鼾在是否中心性肥胖两组间差异具有统计学意义（P<0.05），而婚姻状况、经济收入、工作状况、午睡以及体力活动水平间差异无统计学意义（P>0.05）。见表1。
2.2 不同SRI分组的睡眠特征比较
与睡眠规律组相比（Q4），睡眠严重不规律组（Q1）人群夜间睡眠时长更短，白天小睡时长更多，睡眠连续性得分更低，并且更多存在打鼾现象（P<0.05）。在午睡与睡眠质量上差异无统计学意义（P>0.05）。见表2。
2.3 SRI与中心性肥胖的剂量反应关系分析
以SRI得分为自变量，是否中心性肥胖作为因变量，控制年龄、性别等协变量后建立二者的回归模型。结果显示，SRI得分与是否中心性肥胖之间的风险存在线性关系（非线性检验：χ2=1.55，PNon-linner=0.191）。当SRI得分>72.67分时，随着SRI得分增加，中心性肥胖风险逐渐降低。见图1。
2.4 SRI与中心性肥胖关联的logistic回归分析
以是否存在中心性肥胖为因变量，SRI为自变量进行logistic回归分析，结果显示，在未进行任何协变量调整模型中，与睡眠严重不规律人群相比，睡眠越规律的人群患中心性肥胖的风险越低，与SRI的Q1组相比，Q3组的OR=0.44（95%CI：0.26～0.73，P=0.002）；Q4组的OR=0.36（95%CI：0.21～0.60，P<0.001）。在对年龄、性别等协变量进行调整后，其结果仍然显著，Q3组的OR=0.56（95%CI：0.32～0.99，P=0.045）；Q4组的OR=0.51（95%CI：0.29～0.90，P=0.019）。
亚组分析结果显示，在男性人群（n=275）以及在年龄≥50岁（n=299）的人群中，睡眠越规律的人群患中心性肥胖的风险越低。与SRI的Q1组相比，在男性中，Q3组的OR=0.41（95%CI：0.19～0.91，P=0.029），Q4组的OR=0.37（95%CI：0.16～0.86，P=0.020）；在≥50岁人群中，Q3组的OR=0.41（95%CI：0.19～0.89，P=0.024），Q4组的OR=0.31（95%CI：0.14～0.71，P=0.005）。而在女性以及年龄<50岁的人群中，睡眠规律性与中心性肥胖的关联无统计学意义（P>0.05）。见图2。
2.5 敏感性分析
修正Poisson回归结果显示，睡眠规律人群患中心性肥胖的风险越低。与SRI的Q1组相比，Q3组的PR=0.83（95%CI:0.69～0.99，P=0.049）；Q4组的PR=0.79（95%CI：0.64～0.96，P=0.021）；在男性以及≥50岁人群中，SRI越高，中心性肥胖发生风险越低。与SRI的Q1组相比，在男性中，Q3组的PR=0.79（95%CI：0.63～0.99，P=0.039）；Q4组的PR= 0.76（95%CI：0.58～0.99，P=0.043）；在≥50岁人群中，Q3组的PR=0.80（95%CI：0.65～0.98，P=0.035）；而Q4组的PR=0.72（95%CI：0.56～0.93，P=0.012）。以WC作结局变量时，两种回归结果均显示，与睡眠不规律人群（Q1）相比，睡眠规律人群（Q4）患中心性肥胖的风险更低。见图2。
3 讨论
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本次研究发现在调整混杂因素后睡眠规律性与中心性肥胖发生之间存在关联，敏感性分析结果也仍然稳健。提示SRI得分越高的人群，中心性肥胖发生风险越低。亚组分析结果显示，SRI与中心性肥胖的关联在男性以及≥50岁的人群中更明显。
既往研究表明，睡眠规律在肥胖的发生过程中发挥重要作用，较大的睡眠变异与较高的BMI指数、腰围以及更大的腹部脂肪面积有关[7,11, 21-22]。传统的睡眠规律性指标，如睡眠持续时间标准差、入睡时间标准差等，仅反映了个人的夜间睡眠是否稳定，而无法捕捉到多段睡眠周期或睡眠连续性较差的人群的睡眠特点。人类的新陈代谢受昼夜节律系统的调控，而不规律的睡眠可导致昼夜节律紊乱，从而影响代谢健康。相较于其他睡眠规律指标，SRI能够利用较为完整的睡眠/觉醒时间表数据，捕捉睡眠时间表的快速变化[9]，能够更完整地呈现24 h睡眠模式的变化。本次研究发现，采用SRI评估的睡眠规律性与中心性肥胖的发生存在关联。其可能的原因为：首先，昼夜节律紊乱可能导致饮食模式的不规律，比如夜间进食行为的增加[23-24]。其次，白天小睡以及夜间睡眠时长过短都与肥胖以及内脏脂肪增加之间存在关联[25-29]，而本次研究发现根据SRI确定的睡眠不规律者存在夜间睡眠较少、白天小睡较多等特点。再次，本次研究发现，睡眠不规律人群存在更多的打鼾行为以及较低的睡眠连续性。由打鼾而引起的睡眠不规律也可能是导致肥胖发生的原因之一。最后，不规律睡眠的人群，其昼夜节律可能也不同步，研究显示睡眠不规律的人群可能表现出不同的光照模式[30]，包括光/暗周期的振幅较低（表明白天和夜间光照的差异较小）、白天光照较少、夜间光照较多（这似乎有助于延迟褪黑激素分泌），而褪黑素在激素调节和能量代谢起着至关重要的作用，包括瘦素信号和分泌，并且在血糖稳态中也起着重要的作用,同时还参与白色脂肪组织活动的调制和脂质代谢[31]。因此，SRI可能更能够提供更多关于睡眠规律的信息，也提示夜间睡眠时频繁清醒、白天小睡、以及睡眠碎片化等情况会使睡眠不规律，进而增加发生中心性肥胖的风险。
本次研究也发现，SRI与中心性肥胖的关联存在年龄以及性别差异。中国睡眠研究会公布的《2024中国居民睡眠腱康白皮书》报告显示，年龄到达50岁以后更容易受夜醒/早醒以及夜间如厕而影响睡眠。随着年龄增长，打鼾、夜醒/早醒以及夜尿等发生风险也呈上升趋势，并且存在明显的性别差异[32-33]。而该些行为可能引起睡眠中断从而影响睡眠规律性，进而导致中心性肥胖的发生。同时，随着年龄不断增长，尤其到达50岁以后，肌肉含量会不断下降同时伴随着全身脂肪量的增加，尤其是内脏脂肪量[34]。此外，脂肪组织功能和沉积也因性别而异[35-36]。男女性对脂肪组织的分布不同,男性更倾向于增加内脏脂肪,从而导致中心性肥胖（腹型肥胖）。此外，肥胖发生多受生活行为方式以及饮食习惯影响，而相较于女性，男性有更多的不良生活行为暴露，而由于上述原因，可能使得男性人群以及50岁以上人群中结局暴露比例增加从而出现性别以及年龄差异。因此，这些结果也可能提示出在男性以及≥50岁人群中通过改善睡眠规律性的干预措施中受益较大。但在本研究背景下，导致这些影响的直接机制证据较少，因此需要更多研究来阐明这些关系以及不同途径间的作用。
本研究也存在一定的局限性。第一，本次研究中的睡眠数据均来自智能手环评估，而相较于多导睡眠监测，智能手环睡眠监测的灵敏度以及准确度上存在不足，但智能手环能够提供更为简易的客观睡眠监测新视角以及新方法，能够直观的展示人群客观睡眠状况并且能够监测符合自然状态下的人群睡眠情况并且能够基于智能手环为人群日常睡眠管理提供一定依据。第二，本次研究样本量较少，需要进一步开展大样本前瞻性研究来探讨其交互作用以及对其他不同亚组差异进行探讨。第三，本次研究为横断面研究，其结果在揭示暴露与结局之间的因果关系受到限制；此外，本次研究人群为单中心研究，其结果外推性可能受到人群差异影响，因此，对于SRI与中心性肥胖间的关联性研究，未来需要在更大样本量的前瞻性研究中进行证实。
由SRI所评估的睡眠规律与中心性肥胖之间存在一定关联，SRI得分越高，其发生中心性肥胖的风险越低，尤其在男性以及≥50岁人群中。这也提示出，睡眠规律性作为睡眠的一个新兴维度，同时也作为一个可改变的健康因素，鼓励人们保持规律睡眠以及改善可能影响睡眠规律的因素，从而降低中心性肥胖发生风险。
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  • 国家自然科学基金项目(81960612)
  • 贵州省科技计划项目(黔科合基础-ZK[2022]一般382)
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doi: 10.20043/j.cnki.MPM.202408460
  • 接收时间：2024-08-26
  • 首发时间：2026-03-18
  • 出版时间：2024-12-10
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  • 收稿日期：2024-08-26
基金
国家自然科学基金项目(81960612)
贵州省科技计划项目(黔科合基础-ZK[2022]一般382)
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    1.贵州医科大学公共卫生与健康学院，环境污染与疾病监控教育部重点实验室，贵州 贵安 561113
    2.贵州省黔南布依族苗族自治州福泉市疾病预防控制中心

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
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