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Article(id=1241036246143791410, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241036242561855785, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202503556, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1743091200000, receivedDateStr=2025-03-28, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773815698418, onlineDateStr=2026-03-18, pubDate=1756051200000, pubDateStr=2025-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773815698418, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773815698418, creator=13701087609, updateTime=1773815698418, updator=13701087609, issue=Issue{id=1241036242561855785, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='16', pageStart='2881', pageEnd='3072', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773815697565, creator=13701087609, updateTime=1773840190562, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241138973712634304, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241036242561855785, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241138973712634305, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241036242561855785, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=2881, endPage=2886, ext={EN=ArticleExt(id=1241036246449975610, articleId=1241036246143791410, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Analysis of influencing factors and development of a prediction model for cardiometabolic multimorbidity among middle-aged and elderly populations, Tibet, columnId=1228016567443718970, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods Advances, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the prevalence and influencing factors of cardiometabolic multimorbidity (CMM) among middle-aged and elderly populations in Xizang, and to develop a nomogram prediction model for assessing CMM risk, thereby providing evidence-based references for disease prevention and control strategies.

Methods

Utilizing data from the Xizang survey site of the Southwest China Natural Population Cohort Study, a total of 3 012 participants were included for statistical analysis. Chi-square tests and multivariate logistic regression were employed to identify determinants of CMM, followed by construction of a nomogram prediction model. The model’s performance was validated through Hosmer-Lemeshow goodness-of-fit testing and calibration curve analysis.

Results

Among 3 012 participants, the prevalence of cardiometabolic multimorbidity (CMM) was 25.1%. Multivariable logistic regression analysis identified the following independent risk factors: advanced age (≥65 years: OR=2.484, 95%CI：1.967-3.138), male gender (OR=1.636, 95%CI：1.363-1.963), nocturnal sleep duration <7 hours (OR=1.527, 95%CI：1.101-2.118), frequent snoring (OR=1.392, 95%CI：1.095-1.769), poor self-rated health (OR=2.141, 95%CI：1.606-2.853), BMI ≥28 kg/m2 (OR=1.993, 95%CI：1.498-2.651), and central obesity (OR=1.674, 95%CI: 1.345-2.083). The model demonstrated satisfactory goodness-of-fit (Hosmer-Lemeshow χ2=2.430, P=0.965) with calibration curve analysis showing excellent agreement between predicted and observed probabilities.

Conclusion

Preventive strategies should prioritize middle-aged and elderly individuals with the identified risk profiles: advanced age (≥65 years), male gender, nocturnal sleep deprivation (<7 hours), frequent snoring, suboptimal self-rated health, overweight/obesity, and central obesity. The nomogram model developed based on these influencing factors demonstrates certain predictive value for assessing CMM risk in middle-aged and elderly populations in Xizang.

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目的

了解西藏中老年人群心血管代谢性共病（CMM）流行情况及影响因素，并建立预测CMM患病风险的列线图模型，为CMM的预防和控制提供参考依据。

方法

利用“西南区域自然人群队列项目”西藏自治区调查点数据，一共纳入3 012人进行统计分析。通过χ2检验和logistic回归探讨CMM的影响因素，构建预测CMM患病风险的列线图模型，采用Hosmer–Lemeshow拟合优度检验和校准曲线评价预测模型。

结果

在3 012名研究对象中，CMM患病率为25.1%。多因素logistic回归模型显示，高龄（65岁及以上OR=2.484，95%CI：1.967~3.138）、男性（OR=1.636，95%CI：1.363~1.963）、夜间睡眠时长<7 h（OR=1.527，95%CI：1.101~2.118）、经常打鼾（OR=1.392，95%CI：1.095~1.769）、自评健康差（OR=2.141，95%CI：1.606~2.853）、BMI ≥ 28 kg/m2（OR=1.993，95%CI：1.498~2.651）、中心性肥胖（OR=1.674，95%CI：1.345~2.083）是西藏中老年人CMM患病的危险因素，拟合优度检验（χ2=2.430，P=0.965）和校准曲线分析结果显示模型拟合较好且有良好的校准度。

结论

应重点关注高龄、男性、夜间睡眠时间不足、经常打鼾、自评健康差、超重肥胖以及中心性肥胖的中老年人群，基于这些影响因素构建的列线图模型对西藏中老年人群CMM患病风险具有一定的预测价值。

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何瑞峰，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=7BNbR9YkHDF/pxo/lo+KuA==, magXml=b2HCNwsgnY7E/5xOf9deQg==, pdfUrl=null, pdf=pAk/7830b7PenPzaEEKkyg==, pdfFileSize=826726, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=wSkP0bbv/rxtAez+TF4ARw==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=i7wLu9Gd9eIPTLfVjAUEzw==, mapNumber=null, authorCompany=null, fund=null, authors=

陈自强（1989—），男，硕士在读，主管医师，研究方向：传染病防控

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Risk factors for cardiometabolic multimorbidity based on LASSO regression and random forest algorithms[J]. Chinese Journal of Disease Control & Prevention, 2025,29(1): 82-88. 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(In Chinese), articleTitle=Analysis of major influencing factors of pediatric viral meningitis and construction of clinical prediction model, refAbstract=null)], funds=[Fund(id=1241057504633869081, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036246143791410, awardId=2017YFC0907302, language=CN, fundingSource=国家重点研发计划(2017YFC0907302), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241057501534278213, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036246143791410, xref=null, ext=[AuthorCompanyExt(id=1241057501538472518, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036246143791410, companyId=1241057501534278213, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Tibet Autonomous Region Center for Disease Control and Prevention, Lhasa, Xizang 850000, China), AuthorCompanyExt(id=1241057501731410505, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036246143791410, companyId=1241057501534278213, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=西藏自治区疾病预防控制中心，西藏 拉萨 850000)])], figs=[ArticleFig(id=1241057503635624650, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036246143791410, language=EN, label=Fig.1, caption=Nomogram prediction model of the CMM disease risk, figureFileSmall=Od/gEHbGXGhcDdXkGv597w==, figureFileBig=DE6fY50b80e4jzgXKIKNGQ==, tableContent=null), ArticleFig(id=1241057503719510736, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036246143791410, language=CN, label=图1, caption=CMM患病风险预测模型列线图, figureFileSmall=Od/gEHbGXGhcDdXkGv597w==, figureFileBig=DE6fY50b80e4jzgXKIKNGQ==, tableContent=null), ArticleFig(id=1241057503811785436, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036246143791410, language=EN, label=Fig.2, caption=Calibration curve of the CMM prediction model, figureFileSmall=EPGEfdAOJovji4np/hAHqA==, figureFileBig=Z/nSGRc5H699inpI1p0j8Q==, tableContent=null), ArticleFig(id=1241057503899865825, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036246143791410, language=CN, label=图2, caption=CMM预测模型校准曲线, figureFileSmall=EPGEfdAOJovji4np/hAHqA==, figureFileBig=Z/nSGRc5H699inpI1p0j8Q==, tableContent=null), ArticleFig(id=1241057504000529129, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036246143791410, language=EN, label=Table 1, caption=

Prevalence of CMM among middle-aged and elderly populations with different characteristics in Xizang

, figureFileSmall=null, figureFileBig=null, tableContent=
组别人数患病数患病率（%）χ2P
年龄（岁）75.313<0.001
45~541 39525818.5
55~641 10330727.8
≥6551419037.0
性别28.677<0.001
女1 82839621.7
男1 18435930.3
文化程度0.0080.996
小学及以下2 75469025.1
初中、高中、中专或职校2235625.1
大专及以上35925.7
婚姻状况1.8840.390
已婚或同居2 65866324.9
丧偶、分居或离异2637327.8
从未结婚911920.9
吸烟状况5.7080.017
不吸烟2 32956024.0
吸烟或者有既往吸烟史68319528.6
饮酒状况0.3240.569
不饮酒2 17855225.3
饮酒83420324.3
夜间睡眠时长（h）10.6450.005
<71966734.2
7~92 53061024.1
>92867827.3
睡眠质量1.2370.266
好1 81144124.4
差1 20131426.1
打鼾情况23.066<0.001
从不1 91543222.6
偶尔64717426.9
经常45014933.1
自评健康31.673<0.001
好1 76839422.3
一般97726026.6
差26710137.8
BMI（kg/m2）46.230<0.001
<2468712117.6
24~<281 72342924.9
≥2860220534.1
是否中心性肥胖44.799<0.001
否88815016.9
是2 12460528.5
), ArticleFig(id=1241057504096998128, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036246143791410, language=CN, label=表1, caption=

西藏不同特征的中老年人CMM患病情况

, figureFileSmall=null, figureFileBig=null, tableContent=
组别人数患病数患病率（%）χ2P
年龄（岁）75.313<0.001
45~541 39525818.5
55~641 10330727.8
≥6551419037.0
性别28.677<0.001
女1 82839621.7
男1 18435930.3
文化程度0.0080.996
小学及以下2 75469025.1
初中、高中、中专或职校2235625.1
大专及以上35925.7
婚姻状况1.8840.390
已婚或同居2 65866324.9
丧偶、分居或离异2637327.8
从未结婚911920.9
吸烟状况5.7080.017
不吸烟2 32956024.0
吸烟或者有既往吸烟史68319528.6
饮酒状况0.3240.569
不饮酒2 17855225.3
饮酒83420324.3
夜间睡眠时长（h）10.6450.005
<71966734.2
7~92 53061024.1
>92867827.3
睡眠质量1.2370.266
好1 81144124.4
差1 20131426.1
打鼾情况23.066<0.001
从不1 91543222.6
偶尔64717426.9
经常45014933.1
自评健康31.673<0.001
好1 76839422.3
一般97726026.6
差26710137.8
BMI（kg/m2）46.230<0.001
<2468712117.6
24~<281 72342924.9
≥2860220534.1
是否中心性肥胖44.799<0.001
否88815016.9
是2 12460528.5
), ArticleFig(id=1241057504201855736, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036246143791410, language=EN, label=Table 2, caption=

Results of the multivariable logistic regression analysis for CMM disease

, figureFileSmall=null, figureFileBig=null, tableContent=
变量参照组BSEWald χ2POR值（95%CI）
年龄（岁）55~6445~540.4880.09924.370<0.0011.629（1.342~1.978）
≥650.9100.11958.394<0.0012.484（1.967~3.138）
性别男性女性0.4920.09327.964<0.0011.636（1.363~1.963）
夜间睡眠时长（h）<77~90.4240.1676.4460.0111.527（1.101~2.118）
>90.0610.1470.1730.6771.063（0.797~1.417）
打鼾情况偶尔从不0.0930.1100.7250.3941.098（0.885~1.361）
经常0.3310.1227.3060.0071.392（1.095~1.769）
自评健康一般好0.2340.0975.8810.0151.264（1.046~1.528）
差0.7610.14726.921<0.0012.141（1.606~2.853）
BMI（kg/m2）24~<28<240.3340.1227.4740.0061.397（1.099~1.776）
≥280.6900.14622.399<0.0011.993（1.498~2.651）
是否中心性肥胖是否0.5150.11221.347<0.0011.674（1.345~2.083）
), ArticleFig(id=1241057504298324738, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036246143791410, language=CN, label=表2, caption=

CMM患病的多因素logistic回归分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
变量参照组BSEWald χ2POR值（95%CI）
年龄（岁）55~6445~540.4880.09924.370<0.0011.629（1.342~1.978）
≥650.9100.11958.394<0.0012.484（1.967~3.138）
性别男性女性0.4920.09327.964<0.0011.636（1.363~1.963）
夜间睡眠时长（h）<77~90.4240.1676.4460.0111.527（1.101~2.118）
>90.0610.1470.1730.6771.063（0.797~1.417）
打鼾情况偶尔从不0.0930.1100.7250.3941.098（0.885~1.361）
经常0.3310.1227.3060.0071.392（1.095~1.769）
自评健康一般好0.2340.0975.8810.0151.264（1.046~1.528）
差0.7610.14726.921<0.0012.141（1.606~2.853）
BMI（kg/m2）24~<28<240.3340.1227.4740.0061.397（1.099~1.776）
≥280.6900.14622.399<0.0011.993（1.498~2.651）
是否中心性肥胖是否0.5150.11221.347<0.0011.674（1.345~2.083）
), ArticleFig(id=1241057504394793736, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036246143791410, language=EN, label=Table 3, caption=

Scoring details of the Nomogram for CMM disease risk

, figureFileSmall=null, figureFileBig=null, tableContent=
列线图中变量列线图评分
年龄（岁）45~54=0，55~64=54，≥65=100
性别女=0，男=54
夜间睡眠时长（h）7~9=0，>9=7，<7=47
打鼾情况从不=0，偶尔=10，经常=36
自评健康好=0，一般=26，差=84
BMI（kg/m2）<24=0，24~27.9=37，≥28=76
是否中心性肥胖否=0，是=57
), ArticleFig(id=1241057504466096913, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036246143791410, language=CN, label=表3, caption=

CMM患病风险的列线图评分情况

, figureFileSmall=null, figureFileBig=null, tableContent=
列线图中变量列线图评分
年龄（岁）45~54=0，55~64=54，≥65=100
性别女=0，男=54
夜间睡眠时长（h）7~9=0，>9=7，<7=47
打鼾情况从不=0，偶尔=10，经常=36
自评健康好=0，一般=26，差=84
BMI（kg/m2）<24=0，24~27.9=37，≥28=76
是否中心性肥胖否=0，是=57
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西藏中老年人心血管代谢性共病影响因素分析及预测模型构建
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陈自强 , 何瑞峰
现代预防医学 | 流行病与统计方法 2025,52(16): 2881-2886
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现代预防医学 | 流行病与统计方法 2025, 52(16): 2881-2886
西藏中老年人心血管代谢性共病影响因素分析及预测模型构建
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陈自强, 何瑞峰
作者信息
  • 西藏自治区疾病预防控制中心，西藏 拉萨 850000

    • 陈自强（1989—），男，硕士在读，主管医师，研究方向：传染病防控

通讯作者:

何瑞峰，E-mail：
Analysis of influencing factors and development of a prediction model for cardiometabolic multimorbidity among middle-aged and elderly populations, Tibet
Zi-qiang CHEN, Rui-feng HE
Affiliations
  • Tibet Autonomous Region Center for Disease Control and Prevention, Lhasa, Xizang 850000, China
出版时间: 2025-08-25 doi: 10.20043/j.cnki.MPM.202503556
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摘要
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目的

了解西藏中老年人群心血管代谢性共病（CMM）流行情况及影响因素，并建立预测CMM患病风险的列线图模型，为CMM的预防和控制提供参考依据。

方法

利用“西南区域自然人群队列项目”西藏自治区调查点数据，一共纳入3 012人进行统计分析。通过χ2检验和logistic回归探讨CMM的影响因素，构建预测CMM患病风险的列线图模型，采用Hosmer–Lemeshow拟合优度检验和校准曲线评价预测模型。

结果

在3 012名研究对象中，CMM患病率为25.1%。多因素logistic回归模型显示，高龄（65岁及以上OR=2.484，95%CI：1.967~3.138）、男性（OR=1.636，95%CI：1.363~1.963）、夜间睡眠时长<7 h（OR=1.527，95%CI：1.101~2.118）、经常打鼾（OR=1.392，95%CI：1.095~1.769）、自评健康差（OR=2.141，95%CI：1.606~2.853）、BMI ≥ 28 kg/m2（OR=1.993，95%CI：1.498~2.651）、中心性肥胖（OR=1.674，95%CI：1.345~2.083）是西藏中老年人CMM患病的危险因素，拟合优度检验（χ2=2.430，P=0.965）和校准曲线分析结果显示模型拟合较好且有良好的校准度。

结论

应重点关注高龄、男性、夜间睡眠时间不足、经常打鼾、自评健康差、超重肥胖以及中心性肥胖的中老年人群，基于这些影响因素构建的列线图模型对西藏中老年人群CMM患病风险具有一定的预测价值。

心血管代谢性共病  /  影响因素  /  预测模型  /  中老年人
Objective

To investigate the prevalence and influencing factors of cardiometabolic multimorbidity (CMM) among middle-aged and elderly populations in Xizang, and to develop a nomogram prediction model for assessing CMM risk, thereby providing evidence-based references for disease prevention and control strategies.

Methods

Utilizing data from the Xizang survey site of the Southwest China Natural Population Cohort Study, a total of 3 012 participants were included for statistical analysis. Chi-square tests and multivariate logistic regression were employed to identify determinants of CMM, followed by construction of a nomogram prediction model. The model’s performance was validated through Hosmer-Lemeshow goodness-of-fit testing and calibration curve analysis.

Results

Among 3 012 participants, the prevalence of cardiometabolic multimorbidity (CMM) was 25.1%. Multivariable logistic regression analysis identified the following independent risk factors: advanced age (≥65 years: OR=2.484, 95%CI：1.967-3.138), male gender (OR=1.636, 95%CI：1.363-1.963), nocturnal sleep duration <7 hours (OR=1.527, 95%CI：1.101-2.118), frequent snoring (OR=1.392, 95%CI：1.095-1.769), poor self-rated health (OR=2.141, 95%CI：1.606-2.853), BMI ≥28 kg/m2 (OR=1.993, 95%CI：1.498-2.651), and central obesity (OR=1.674, 95%CI: 1.345-2.083). The model demonstrated satisfactory goodness-of-fit (Hosmer-Lemeshow χ2=2.430, P=0.965) with calibration curve analysis showing excellent agreement between predicted and observed probabilities.

Conclusion

Preventive strategies should prioritize middle-aged and elderly individuals with the identified risk profiles: advanced age (≥65 years), male gender, nocturnal sleep deprivation (<7 hours), frequent snoring, suboptimal self-rated health, overweight/obesity, and central obesity. The nomogram model developed based on these influencing factors demonstrates certain predictive value for assessing CMM risk in middle-aged and elderly populations in Xizang.

Cardiometabolic multimorbidity  /  Influencing factors  /  Prediction model  /  Middle-aged and elderly populations
陈自强, 何瑞峰. 西藏中老年人心血管代谢性共病影响因素分析及预测模型构建. 现代预防医学, 2025 , 52 (16) : 2881 -2886 . DOI: 10.20043/j.cnki.MPM.202503556
Zi-qiang CHEN, Rui-feng HE. Analysis of influencing factors and development of a prediction model for cardiometabolic multimorbidity among middle-aged and elderly populations, Tibet[J]. Modern Preventive Medicine, 2025 , 52 (16) : 2881 -2886 . DOI: 10.20043/j.cnki.MPM.202503556
正文
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随着我国人口老龄化的加速，中老年人群患有多种慢性疾病越发普遍，共病成为我国公共卫生领域重大的挑战之一。心血管代谢性共病（cardiometabolic multimorbidity，CMM）是指同一个体患有两种或两种以上的心血管代谢性疾病[1]，是中老年人群中常见的一种共病聚集模式[2]。CMM不仅加剧个体健康风险，还导致治疗复杂性增加、医疗成本攀升，成为健康老龄化的主要障碍之一[3-4]。根据一项Meta分析结果显示，我国中老年人群中CMM的患病率约14.6%，且随着时间呈显著上升趋势[5]。CMM因其高发病率、高死亡率，对中老年人的身体健康造成严重危害，若不能及时干预，会降低中老年人的生命质量和预期寿命，因此，早期识别可能存在的危险因素对于延缓疾病的发生和发展非常重要。预测模型是评估特定疾病或健康状况发生可能性的工具，可以帮助医疗保健人员或个人早期识别高危人群，从而达到早预防的目的[6]。现有预测模型多聚焦于单一疾病，且模型变量多局限于临床指标，对个人行为生活方式等可干预因素纳入不足，这限制了其在早期干预中的应用价值。基于此，本研究拟通过对西藏中老年人群CMM影响因素的探讨，并构建适用于该人群的CMM患病风险预测模型，旨在早期识别中老年CMM高风险人群，为高原地区心血管代谢性疾病的防控提供一定的科学依据和参考。
1　对象与方法
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1.1　研究对象
本研究使用“西南区域自然人群队列研究”项目中西藏调查点数据，研究对象纳入标准：①年龄为45岁及以上；②藏族居民；③根据研究目的，所研究的变量无缺失者。一共纳入研究对象3 012人。
1.2　调查方法
项目采用分层整群抽样的方法，根据乡镇（社区）的规模，抽取了娘热、纳金、蔡公堂、夺底和洛堆社区五个乡镇（社区）。项目调查时间为2018年5月—2019年9月，采用项目办设计的电子化问卷收集受访者的一般情况（性别、年龄、婚姻状况等）、行为生活方式（吸烟和饮酒状况）、睡眠情况、自评健康等。身体检查指标包括身高、体重、腰围、血压等。血生化指标是由专业人员采集调查对象空腹静脉血，并送至有资质的检测机构进行检测。调查的全过程中进行了严格的质量控制，包括由经过培训且熟悉方言的专业人员进行现场调查，调查时录音质控、数据清洗和逻辑纠错等多种方式[7]。本研究通过了四川大学医学伦理审查委员会的伦理审批（审查编号为K2020022），研究对象均签署了知情同意书。
1.3　指标定义
①血脂异常指TG≥1.7 mmol/L或LDL-C≥3.4 mmol/L或HDL-C<1.0 mmol/L[8]。②高血压指收缩压≥140 mmHg或（和）舒张压≥90 mmHg或自我报告被乡/区级或以上医院诊断患有高血压[9]。③糖尿病指空腹血糖≥7.0 mmol/L[10]或自我报告被乡/区级或以上医院诊断患有糖尿病。④自我报告被乡/区级或以上医院诊断患有中风则定义为中风。⑤自我报告被乡/区级或以上医院诊断患有冠心病则定义为冠心病。参照既往研究[11]，将患有以上5种疾病中的两种或两种以上则定义为患有CMM。早醒、入睡困难、日间困倦、药物助眠，这4种睡眠问题中的任意一种或多种则定义为睡眠质量差。中心性肥胖是指男性腰围≥90 cm，女性腰围≥85 cm[12]。
1.4　统计分析
分类变量采用率或者构成比描述，组间的比较采用χ2检验。采用向前逐步logistic回归筛选CMM的影响因素和预测因子，建立列线图模型，Hosmer–Lemeshow拟合优度检验和校准曲线用于评价预测模型的构建效果。检验水准α=0.05，数据的整理与分析使用SPSS 24.0和R 4.2.3软件。列线图的绘制和各个变量的评分分别通过R软件中的“rms”和“nomogramEx”包完成。
2　结果
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2.1　研究对象基本情况
在3 012名研究对象中，患有CMM者755人，患病率为25.1%。在年龄65岁及以上、男性、吸烟或者有既往吸烟史、夜间睡眠时长<7 h、经常打鼾、自评健康差、BMI≥28 kg/m2、中心性肥胖这些组别中，研究对象的CMM患病率更高，差异具有统计学意义（P<0.05）。见表1。
2.2　CMM患病影响因素的多因素分析
是否患有CMM作为因变量，年龄、性别、吸烟状况、夜间睡眠时长、打鼾情况、自评健康、BMI以及是否中心性肥胖作为自变量，采用向前法，构建logistic回归模型。结果显示，CMM患病风险随着年龄和BMI的增加而增加；自评健康越差，CMM患病风险越高；男性（OR=1.636，95%CI：1.363~1.963）、夜间睡眠时长<7 h（OR=1.527，95%CI：1.101~2.118）、经常打鼾（OR=1.392，95%CI：1.095~1.769）、中心性肥胖（OR=1.674，95%CI：1.345~2.083）是西藏中老年人CMM患病的危险因素。见表2。
2.3　CMM患病风险的预测模型构建
根据logistic回归得到的7个预测因子建立列线图模型，列线图中各变量的评分是基于logistic回归模型中各变量的偏回归系数通过比例转换生成。列线图分别对应有7个评分项，根据个体的实际情况，找出7个变量的对应刻度点，向上画出垂直线，得出单项变量的评分，将所有变量评分相加得到总评分，CMM患病风险是通过总评分向下画一条垂直线来获得。总评分分值越高，则CMM患病风险越高。见图1和表3。
2.4　CMM预测模型效果评价
Hosmer-Lemeshow拟合优度检验表明，预测模型拟合良好（χ2=2.430，P=0.965）。通过Bootstrap法进行内部验证，设置重抽样次数为1 000，如图2所示，预测模型校准曲线显示预测概率和实际概率高度一致，表明该模型校准度良好，对CMM具有一定的预测价值。
3　讨论
收起
近年来，关于CMM的研究受到了我国学者的广泛关注。一项基于中国健康与养老追踪调查（China Health and Retirement Longitudinal Study，CHARLS）数据库的研究结果显示，中老年人群的CMM患病率为24.5%[13]。郭丹等人利用2018年CHARLS数据研究发现，老年人群CMM患病率为33.94%[11]。一项关于重庆市中老年人群的研究发现，CMM患病率为11.2%[14]。不同研究中CMM患病率差异较大，可能与各项研究对CMM的定义和调查人群的特征不同有关。西藏中老年人CMM患病率为25.1%，处于较高的水平，低氧环境和饮食可能是重要的诱因。一方面，高原低氧低压的自然环境下，人体会发生相关代偿性反应，增加了高血压、冠心病等心血管事件的发生率[15]，另外一方面，西藏居民喜食酥油、奶渣、甜茶等高热量食物，与代谢紊乱疾病密切相关[16]。男性CMM患病率高于女性，这可能与性别间生理性差异有关，影响女性的主要激素是雌激素，雌激素有助于调节脂质代谢、保护血管内皮细胞，从而延缓粥样动脉硬化的发生，在保护心血管代谢健康中发挥重要作用[17]，此外，男性通常更容易产生吸烟和过量饮酒等不健康的行为。另外，年龄也是CMM患病的影响因素，CMM患病率随着年龄的增加而升高。高年龄组人群的患病率显著较高，这可能与生理衰老过程中人体机能下降有关。随着年龄的增长，血管硬化、代谢功能下降以及心肌功能退化等生理过程都会增加多种心血管代谢性疾病的风险[18]。本研究发现夜间睡眠时长不足和经常打鼾是CMM患病的危险因素。睡眠不足会导致人体生理节律失衡，同时引起机体炎症反应，血管损伤，代谢紊乱等一系列病理变化[19-20]，而中重度打鼾则认为是阻塞性睡眠呼吸暂停综合症的预测指标之一，经常打鼾会引起高碳酸血症和低氧血症，进而导致糖脂代谢紊乱和心血管事件[21]。自评健康越差者CMM患病率更高，自评健康是主观评价健康的指标，能够反映个体对自身健康状况的整体感知[22]。已有研究表明，自评健康差与高血压、糖尿病、冠心病等多种心血管代谢性疾病密切相关[23]。此外，BMI越高、中心性肥胖是CMM的危险因素，这与既往研究一致[24]。过量的脂肪沉积，尤其是腹部脂肪过多，会导致胰岛素抵抗、炎症反应增加、糖脂代谢异常等一系列问题，从而显著提高心血管代谢性共病的发生率[25-26]。
目前，关于CMM的风险预测研究相对有限，且使用的预测方法为随机森林算法[27]和哈佛癌症风险指数法[28]，这些预测模型均表现出了较好的预测性能。与上述方法相比，列线图具有直观性强、可解释性高、实际操作性好等优点。列线图通过可视化的形式展示，根据个人情况，能非常直观的显示某种疾病发生概率的大小，容易解释，特别适用于疾病的早期发病风险预测[29]。尽管列线图在多种疾病的风险预测中得到较为广泛的应用，但以列线图作为预测CMM风险工具的研究仍然较欠缺。本研究所绘制的列线图模型结合了多项因素，这些因素或信息非常容易获取，可以帮助医务人员及个人快速地对患病风险进行初步评估，进而制定个性化的健康管理和干预策略。假如一名受试者年龄66岁、男性、夜间睡眠时长<7 h、偶尔打鼾、自评健康一般、BMI为30 kg/m2且有中心性肥胖，那么其得分为100+54+47+10+26+76+57=370，预测患病风险66.7%。
综上所述，本研究基于CMM的影响因素，建立了列线图模型，为西藏中老年人群体的CMM风险预测提供了简便且有效的工具。未来，仍需要重点关注行为生活方式等可干预因素与CMM的因果关联及内在影响机制，从而做好CMM的预防和管理。然而，本研究也存在一些局限性。首先，本研究为横断面研究，无法推断因果关系。其次，除体格检查和血生化检测外，问卷资料均通过自我报告的方式收集，可能存在回忆偏倚。
基金
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  • 国家重点研发计划(2017YFC0907302)
文献
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doi: 10.20043/j.cnki.MPM.202503556
  • 接收时间：2025-03-28
  • 首发时间：2026-03-18
  • 出版时间：2025-08-25
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  • 收稿日期：2025-03-28
基金
国家重点研发计划(2017YFC0907302)
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    西藏自治区疾病预防控制中心，西藏 拉萨 850000

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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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