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Article(id=1241522923526222762, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241522919977841545, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202310100, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1696867200000, receivedDateStr=2023-10-10, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773931731348, onlineDateStr=2026-03-19, pubDate=1710000000000, pubDateStr=2024-03-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773931731348, onlineIssueDateStr=2026-03-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773931731348, creator=13701087609, updateTime=1773931731348, 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=829, endPage=835, ext={EN=ArticleExt(id=1241522923874350006, articleId=1241522923526222762, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Establishment and verification of risk prediction model for abnormal bone mass of ethnic minority postmenopausal women in Guizhou Province, columnId=1228016568949474136, journalTitle=Modern Preventive Medicine, columnName=Child and Adolescent Health, Maternal and Child Health, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the prevalence and influencing factors of abnormal bone mass in postmenopausal women of Dong and Miao ethnic groups in Guizhou Province, and to establish a nomogram prediction model and verify it, so as to provide theoretical basis for the prevention of abnormal bone mass in ethnic minority postmenopausal women in Guizhou Province.

Methods

This study was a cross-sectional study. In total 3 208 postmenopausal women of Dong and Miao ethnic groups were selected from the cohort study database of natural population of ethnic minorities in southwest China, and ultrasonic bone densitometer was used to measure calcaneal bone mineral density (BMD). The concentrations of cadmium, cobalt, copper, chromium, iron, mercury, manganese, molybdenum, nickel, lead, strontium, vanadium, and zinc in urine were determined by inductively coupled plasma mass spectrometer, and the relationship between urine metal, demographic characteristics, reproductive characteristics, ethnic diet, and BMD was analyzed by binary logistic regression, and the risk prediction model of bone mass abnormality was established.

Results

A total of 3 208 people were included in the study, and a total of 1 520 people with abnormal bone mass were detected. The detection rate of abnormal bone mass was 47.38%. Based on age (OR=1.098, 95%CI: 1.083-1.113),history of bone fracture (OR=0.560, 95%CI: 0.395-0.794), age of menopause (OR=0.965, 95%CI: 0.949-0.982), number of live births (OR=0.900, 95%CI: 0.818-0.990), use of contraceptive (OR=0.708, 95%CI: 0.555-0.901), total physical activity (OR=0.989, 95%CI: 0.984-0.994), urinary cadmium (OR:1.060, 95%CI:1.009-1.113), and urinary chromium (OR=1.004, 95%CI:1.001-1.008) were independent influencing factors of abnormal bone mass. To establish a predictive model Logit (P) for predicting the risk of abnormal bone mass in postmenopausal women: Logit (P) =-2.170+0.093 * (age)-0.580 * (history of bone fracture)-0.035 * (menopausal age)-0.105 * (number of live births)-0.346* (whether or not you have taken contraceptive)-0.011 *(total physical activity) + 0.058 * (urinary cadmium) + 0.004 * (urinary chromium). The area under the receiver operating characteristic curve of the model was 0.722. The nomogram was drawn and verified internally. The results showed that the calibration curve predicted by the model for abnormal bone mass was basically consistent with the actual curve, and the fitting effect was good.

Conclusion

Cadmium and chromium are independent risk factors of abnormal bone mass, indicating that metal exposure can affect the occurrence and development of abnormal bone mass. The prediction model and score diagram of bone mass abnormality have good predictive ability, which provides a reference for screening abnormal bone mass of minority postmenopausal women in Guizhou Province.

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目的

了解贵州省侗族和苗族绝经女性骨量异常的流行情况及影响因素，建立列线图预测模型并验证，为贵州省少数民族绝经女性骨量异常的预防提供理论依据。

方法

本研究为横断面研究，从西南区域少数民族聚集地世居自然人群队列研究数据库中抽取3 208名侗族、苗族绝经女性为研究对象；使用超声骨密度仪检测跟骨骨密度。尿液中镉、钴、铜、铬、铁、汞、锰、钼、镍、铅、锶、钒、锌元素的浓度使用电感耦合等离子体质谱仪测定；采用二元logistic回归分析尿液金属、人口学特征、生殖特征、民族特色饮食与骨量异常的关联，并构建骨量异常风险预测模型。

结果

研究共纳入3 208人，检出骨量异常共1 520人，骨量异常检出率为47.38%；年龄（OR=1.098，95%CI：1.083～1.113）、骨折史（OR=0.560，95%CI：0.395～0.794）、绝经年龄（OR=0.965，95%CI：0.949～0.982）、活产次数（OR=0.900，95%CI：0.818～0.990）、是否服用避孕药（OR=0.708，95%CI：0.555～0.901）、总体力活动（OR=0.989，95%CI：0.984～0.994）、尿镉（OR=1.060，95%CI：1.009～1.113）和尿铬（OR=1.004，95%CI：1.001～1.008）是骨量异常的独立影响因素，建立预测绝经女性骨量异常患病风险的预测模型Logit (P)=-2.170+0.093×（年龄）-0.580×（骨折史）-0.035×（绝经年龄）-0.105×（活产次数）-0.346×（是否服用过避孕药）-0.011×（总体力活动）+0.058×（尿镉）+0.004×（尿铬），该模型受试者工作特征曲线下面积为0.722，绘制列线图并进行内部验证，结果提示该列线图模型预测骨量异常发生的校准曲线与实际曲线基本吻合，拟合效果良好。

结论

镉、铬是骨量异常的独立危险因素，表明金属暴露会影响骨量异常的发生发展，骨量异常预测模型及评分列线图均得到了较好的预测能力，为筛查贵州省少数民族绝经女性骨量异常提供了参考。

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洪峰，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=HIBITQ014kWE2SvEz5TjfA==, magXml=j6HXHvdQAj5uNVWeX2tr2g==, pdfUrl=null, pdf=DpPU3Zu6EcTYAYmYv9MA3A==, pdfFileSize=1393461, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=Z76q8Yo85Mu7eLJvWqnpjg==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=87/gZA74z76K0IAwo+mg8w==, mapNumber=null, authorCompany=null, fund=null, authors=

张宇馨（1998—），女，硕士在读，研究方向：环境与人群毒理学

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张宇馨（1998—），女，硕士在读，研究方向：环境与人群毒理学

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张宇馨（1998—），女，硕士在读，研究方向：环境与人群毒理学

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Endocrine, 2019, 63(2): 213-224., articleTitle=Association between age at menopause and fracture risk: a systematic review and meta-analysis, refAbstract=null), Reference(id=1241678340822725520, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923526222762, doi=null, pmid=null, pmcid=null, year=2021, volume=54, issue=4, pageStart=284, pageEnd=292, url=null, language=null, rfNumber=[22], rfOrder=27, authorNames=Seo E, Lee YR, Kim HC, journalName=Journal of Preventive Medicine and Public Health, refType=null, unstructuredReference=Seo E, Lee YR, Kim HC. Association between parity and low bone density among postmenopausal Korean women[J]. 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Maturitas, 2015, 82(2):236-240., articleTitle=Interpregnancy interval as a risk factor for postmenopausal osteoporosis, refAbstract=null)], funds=[Fund(id=1241678324741763798, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923526222762, awardId=2017YFC0907301, language=CN, fundingSource=国家重点研发计划课题(2017YFC0907301), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241678285910896953, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923526222762, xref=null, ext=[AuthorCompanyExt(id=1241678285923479868, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923526222762, companyId=1241678285910896953, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Public Health and Health, Guizhou Medical University, Guiyang, Guizhou 561113, China), AuthorCompanyExt(id=1241678285931868478, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923526222762, companyId=1241678285910896953, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=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 贵阳 561113)])], figs=[ArticleFig(id=1241678311647146597, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923526222762, language=EN, label=Figure 1, caption=ROC curves of the prediction model for the incidence of abnormal bone mass in menopausal women, figureFileSmall=O5phOhkzw71UpgeVB8AXfg==, figureFileBig=jHWw4Dz5egt1pTvJQoBEiA==, tableContent=null), ArticleFig(id=1241678312016245359, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923526222762, language=CN, label=图1, caption=绝经女性骨量异常发生率预测模型的ROC曲线, figureFileSmall=O5phOhkzw71UpgeVB8AXfg==, figureFileBig=jHWw4Dz5egt1pTvJQoBEiA==, tableContent=null), ArticleFig(id=1241678315870810743, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923526222762, language=EN, label=Figure 2, caption=Nomogram of the incidence of abnormal bone mass in menopausal women, figureFileSmall=8/LxZcPTFXr0X3jhvUi17w==, figureFileBig=fbspnbaDmzZDjdIRhfoe5g==, tableContent=null), ArticleFig(id=1241678316801946238, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923526222762, language=CN, label=图2, caption=绝经女性骨量异常发生率列线图

注：尿液金属浓度经自然对数转换。

, figureFileSmall=8/LxZcPTFXr0X3jhvUi17w==, figureFileBig=fbspnbaDmzZDjdIRhfoe5g==, tableContent=null), ArticleFig(id=1241678319582769798, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923526222762, language=EN, label=Figure 3, caption=Calibration curves for Nomogram for predicting the incidence of abnormal bone mass in menopausal women, figureFileSmall=uQm6YWzE1rjOgL0RhWW29w==, figureFileBig=WrYBpwFCI1pyrlPT+5AeQQ==, tableContent=null), ArticleFig(id=1241678319897342602, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923526222762, language=CN, label=图3, caption=绝经女性骨量异常发生率预测列线图模型校准曲线, figureFileSmall=uQm6YWzE1rjOgL0RhWW29w==, figureFileBig=WrYBpwFCI1pyrlPT+5AeQQ==, tableContent=null), ArticleFig(id=1241678320325161616, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923526222762, language=EN, label=Table 1, caption=

Basic characteristics of the study population ［M（P25，P75），n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
特征骨量正常(n=1 688)骨量异常(n=1 520)χ2/Z值P值
年龄55.61(52.45,62.05)62.66(55.57,68.70)-17.779<0.001
骨密度T值-0.30(-0.60,0.30)-1.50(-1.80,-1.20)-47.975<0.001
民族10.8630.001
侗1 125(66.65)928(61.05)
苗563(33.35)592(38.95)
户口类型2.2470.325
农业户口1 344(79.72)1 182(77.86)
非农业户口131(7.77)119(7.84)
统一居民户口211(12.51)217(14.30)
婚姻状态38.404<0.001
已婚/同居1 399(82.93)1 128(74.21)
分居/离异37(2.19)40(2.63)
丧偶251(14.88)352(23.16)
教育程度1.6520.438
低于高中学历1 496(88.63)1 366(89.87)
高中及大专178(10.54)140(9.21)
本科及以上14(0.83)14(0.92)
吸烟
否1 684(99.76)1 516(99.74)0.0220.882
目前吸烟4(0.24)4(0.26)
饮酒11.8910.003
从不饮酒1 197(70.91)1 149(75.79)
偶尔饮酒440(26.07)318(20.92)
经常饮酒51(3.02)53(3.49)
高血压5.4390.020
否1 330(78.79)1 145(75.33)
是358(21.21)375(24.67)
糖尿病1.7680.184
否1 613(95.56)1 437(94.54)
是75(4.44)83(5.46)
骨折史14.602<0.001
否1 620(95.97)1 412(92.89)
是68(4.03)108(7.11)
总体力活动72.229<0.001
低400(23.94)547(36.39)
中411(24.60)382(25.42)
高860(51.46)574(38.19)
BMI13.4010.004
正常715(42.36)718(47.31)
偏瘦45(2.66)57(3.75)
超重659(39.04)516(33.99)
肥胖269(15.94)227(14.95)
腰围11.3900.003
正常556(32.98)584(38.44)
中度肥胖660(39.14)526(34.63)
重度肥胖470(27.88)409(26.93)
腌制蔬菜2.00(0.42,4.00)2.00(0.42,5.00)-0.1910.358
糯米及制品1.00(0.33,3.00)1.00(0.25,3.00)-0.5310.596
腌制鱼/肉类0.50(0.00,2.00)0.33(0.00,2.00)-3.572<0.001
腊/熏肉0.83(0.25,2.00)0.83(0.17,2.00)-1.5710.116
酸汤（白酸）0.00(0.00,1.00)0.00(0.00,2.00)-2.7770.005
酸汤（红酸）1.00(0.00,3.00)1.00(0.00,3.25)-0.9010.368
牛/羊瘪菜0.17(0.00,0.67)0.08(0.00,0.67)-2.1830.029
紫血肉0.00(0.00,0.33)0.00(0.00,0.25)-0.4730.636
蕨菜0.50(0.25,1.00)0.50(0.25,1.00)-1.7780.075
油茶0.08(0.00,4.00)0.00(0.00,3.00)-2.1110.035
), ArticleFig(id=1241678320681677463, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923526222762, language=CN, label=表1, caption=

研究对象基本特征［M（P25，P75），n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
特征骨量正常(n=1 688)骨量异常(n=1 520)χ2/Z值P值
年龄55.61(52.45,62.05)62.66(55.57,68.70)-17.779<0.001
骨密度T值-0.30(-0.60,0.30)-1.50(-1.80,-1.20)-47.975<0.001
民族10.8630.001
侗1 125(66.65)928(61.05)
苗563(33.35)592(38.95)
户口类型2.2470.325
农业户口1 344(79.72)1 182(77.86)
非农业户口131(7.77)119(7.84)
统一居民户口211(12.51)217(14.30)
婚姻状态38.404<0.001
已婚/同居1 399(82.93)1 128(74.21)
分居/离异37(2.19)40(2.63)
丧偶251(14.88)352(23.16)
教育程度1.6520.438
低于高中学历1 496(88.63)1 366(89.87)
高中及大专178(10.54)140(9.21)
本科及以上14(0.83)14(0.92)
吸烟
否1 684(99.76)1 516(99.74)0.0220.882
目前吸烟4(0.24)4(0.26)
饮酒11.8910.003
从不饮酒1 197(70.91)1 149(75.79)
偶尔饮酒440(26.07)318(20.92)
经常饮酒51(3.02)53(3.49)
高血压5.4390.020
否1 330(78.79)1 145(75.33)
是358(21.21)375(24.67)
糖尿病1.7680.184
否1 613(95.56)1 437(94.54)
是75(4.44)83(5.46)
骨折史14.602<0.001
否1 620(95.97)1 412(92.89)
是68(4.03)108(7.11)
总体力活动72.229<0.001
低400(23.94)547(36.39)
中411(24.60)382(25.42)
高860(51.46)574(38.19)
BMI13.4010.004
正常715(42.36)718(47.31)
偏瘦45(2.66)57(3.75)
超重659(39.04)516(33.99)
肥胖269(15.94)227(14.95)
腰围11.3900.003
正常556(32.98)584(38.44)
中度肥胖660(39.14)526(34.63)
重度肥胖470(27.88)409(26.93)
腌制蔬菜2.00(0.42,4.00)2.00(0.42,5.00)-0.1910.358
糯米及制品1.00(0.33,3.00)1.00(0.25,3.00)-0.5310.596
腌制鱼/肉类0.50(0.00,2.00)0.33(0.00,2.00)-3.572<0.001
腊/熏肉0.83(0.25,2.00)0.83(0.17,2.00)-1.5710.116
酸汤（白酸）0.00(0.00,1.00)0.00(0.00,2.00)-2.7770.005
酸汤（红酸）1.00(0.00,3.00)1.00(0.00,3.25)-0.9010.368
牛/羊瘪菜0.17(0.00,0.67)0.08(0.00,0.67)-2.1830.029
紫血肉0.00(0.00,0.33)0.00(0.00,0.25)-0.4730.636
蕨菜0.50(0.25,1.00)0.50(0.25,1.00)-1.7780.075
油茶0.08(0.00,4.00)0.00(0.00,3.00)-2.1110.035
), ArticleFig(id=1241678321562481311, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923526222762, language=EN, label=Table 2, caption=

Association between reproductive factors and bone mass abnormalities in the study population ［n（%），M（P25，P75）］

, figureFileSmall=null, figureFileBig=null, tableContent=
特征骨量正常(n=1 688)骨量异常(n=1 520)χ2/Z值P值
是否服用过避孕药16.983<0.001
否1 413(51.16)1 349(48.84)
是275(61.66)171(38.34)
初潮年龄15.00(14.00,17.00)16.00(14.00,18.00)4.132<0.001
首次生育年龄23.00(25.00,27.00)23.00(25.00,27.00)2.3360.019
绝经年龄49.00(45.00,50.00)48.00(45.00,50.00)2.9830.003
怀孕次数4.00(3.00,5.00)4.00(3.00,5.00)5.257<0.001
活产次数3.00(2.00,3.00)3.00(2.00,4.00)6.976<0.001
), ArticleFig(id=1241678321981911717, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923526222762, language=CN, label=表2, caption=

研究对象生殖特征与骨量异常的关联性［n（%），M（P25，P75）］

, figureFileSmall=null, figureFileBig=null, tableContent=
特征骨量正常(n=1 688)骨量异常(n=1 520)χ2/Z值P值
是否服用过避孕药16.983<0.001
否1 413(51.16)1 349(48.84)
是275(61.66)171(38.34)
初潮年龄15.00(14.00,17.00)16.00(14.00,18.00)4.132<0.001
首次生育年龄23.00(25.00,27.00)23.00(25.00,27.00)2.3360.019
绝经年龄49.00(45.00,50.00)48.00(45.00,50.00)2.9830.003
怀孕次数4.00(3.00,5.00)4.00(3.00,5.00)5.257<0.001
活产次数3.00(2.00,3.00)3.00(2.00,4.00)6.976<0.001
), ArticleFig(id=1241678322405536432, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923526222762, language=EN, label=Table 3, caption=

Urine metal distribution in the study population

, figureFileSmall=null, figureFileBig=null, tableContent=
金属(μg/L)LOD≥LOD ［n（%）］M（P25，P75）P值
总人群骨量正常骨量异常
Cd0.011 13 143(97.97)1.94(1.14,3.18)1.81(1.02,3.04)2.05(1.25,3.37)<0.001
Co0.000 12 872(89.53)0.36(0.17,0.64)0.33(0.15,0.60)0.39(0.19,0.69)<0.001
Cr0.002 13 108(96.88)39.05(24.16,59.56)35.62(22.74,55.52)42.46(26.33,63.44)<0.001
Cu0.001 62 889(90.06)80.82(27.79,206.06)76.27(25.86,204.09)85.23(29.88,210.17)0.078
Fe0.020 13 183(99.22)418.71(211.57,694.48)399.00(202.25,665.09)438.07(218.76,721.03)0.022
Hg0.016 82 590(80.74)0.45(0.07,1.27)0.43(0.05,1.19)0.47(0.09,1.38)0.145
Mn0.000 22 656(82.79)4.70(1.44,10.32)4.38(1.16,9.59)5.10(1.74,10.82)<0.001
Mo0.076 73 208(100.00)68.00(40.47,104.23)64.48(38.30,99.58)71.50(43.37,110.05)<0.001
Ni0.000 21 939(60.44)1.41(0.000 3,6.42)0.94(0.000 3,5.74)1.94(0.000 3,6.96)<0.001
Pb0.000 12 771(86.38)3.32(1.34,6.59)3.13(1.26,6.26)3.54(1.45,6.83)0.007
Sr0.161 03 206(99.93)86.28(48.88,136.68)84.02(47.84,133.52)88.47(50.47,140.99)0.124
V0.016 63 206(99.93)35.08(17.61,57.52)33.58(16.63,56.56)36.18(18.79,58.29)0.006
Zn0.018 03 205(99.91)636.47(407.46,939.95)622.10(404.36,928.37)648.05(409.06,944.56)0.141
), ArticleFig(id=1241678323017904820, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923526222762, language=CN, label=表3, caption=

研究人群尿液金属分布

, figureFileSmall=null, figureFileBig=null, tableContent=
金属(μg/L)LOD≥LOD ［n（%）］M（P25，P75）P值
总人群骨量正常骨量异常
Cd0.011 13 143(97.97)1.94(1.14,3.18)1.81(1.02,3.04)2.05(1.25,3.37)<0.001
Co0.000 12 872(89.53)0.36(0.17,0.64)0.33(0.15,0.60)0.39(0.19,0.69)<0.001
Cr0.002 13 108(96.88)39.05(24.16,59.56)35.62(22.74,55.52)42.46(26.33,63.44)<0.001
Cu0.001 62 889(90.06)80.82(27.79,206.06)76.27(25.86,204.09)85.23(29.88,210.17)0.078
Fe0.020 13 183(99.22)418.71(211.57,694.48)399.00(202.25,665.09)438.07(218.76,721.03)0.022
Hg0.016 82 590(80.74)0.45(0.07,1.27)0.43(0.05,1.19)0.47(0.09,1.38)0.145
Mn0.000 22 656(82.79)4.70(1.44,10.32)4.38(1.16,9.59)5.10(1.74,10.82)<0.001
Mo0.076 73 208(100.00)68.00(40.47,104.23)64.48(38.30,99.58)71.50(43.37,110.05)<0.001
Ni0.000 21 939(60.44)1.41(0.000 3,6.42)0.94(0.000 3,5.74)1.94(0.000 3,6.96)<0.001
Pb0.000 12 771(86.38)3.32(1.34,6.59)3.13(1.26,6.26)3.54(1.45,6.83)0.007
Sr0.161 03 206(99.93)86.28(48.88,136.68)84.02(47.84,133.52)88.47(50.47,140.99)0.124
V0.016 63 206(99.93)35.08(17.61,57.52)33.58(16.63,56.56)36.18(18.79,58.29)0.006
Zn0.018 03 205(99.91)636.47(407.46,939.95)622.10(404.36,928.37)648.05(409.06,944.56)0.141
), ArticleFig(id=1241678323315700412, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923526222762, language=EN, label=Table 4, caption=

Results of binary logistic regression analysis of bone mass abnormalities in menopausal women

, figureFileSmall=null, figureFileBig=null, tableContent=
变量β标准误差wald χ2值P值OR值(95%CI)
年龄0.0930.007172.841<0.0011.098(1.083～1.113)
骨折史-0.5800.17810.5950.0010.560(0.395～0.794)
绝经年龄-0.0350.00916.019<0.0010.965(0.949～0.982)
活产次数-0.1050.0494.6860.0300.900(0.818～0.990)
服用避孕药-0.3460.1247.8390.0050.708(0.555～0.901)
总体力活动-0.0110.00317.643<0.0010.989(0.984～0.994)
镉0.0580.0255.4210.0201.060(1.009～1.113)
铬0.0040.0025.8570.0161.004(1.001～1.008)
常量-2.1700.8097.1970.007
), ArticleFig(id=1241678323831599814, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923526222762, language=CN, label=表4, caption=

绝经女性骨量异常二元logistic回归分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
变量β标准误差wald χ2值P值OR值(95%CI)
年龄0.0930.007172.841<0.0011.098(1.083～1.113)
骨折史-0.5800.17810.5950.0010.560(0.395～0.794)
绝经年龄-0.0350.00916.019<0.0010.965(0.949～0.982)
活产次数-0.1050.0494.6860.0300.900(0.818～0.990)
服用避孕药-0.3460.1247.8390.0050.708(0.555～0.901)
总体力活动-0.0110.00317.643<0.0010.989(0.984～0.994)
镉0.0580.0255.4210.0201.060(1.009～1.113)
铬0.0040.0025.8570.0161.004(1.001～1.008)
常量-2.1700.8097.1970.007
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贵州省少数民族绝经女性骨量异常风险预测模型的建立与验证
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张宇馨 , 陈成 , 吴申燕 , 胡宇欣 , 洪峰
现代预防医学 | 儿少卫生与妇幼保健 2024,51(5): 829-835
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现代预防医学 | 儿少卫生与妇幼保健 2024, 51(5): 829-835
贵州省少数民族绝经女性骨量异常风险预测模型的建立与验证
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张宇馨, 陈成, 吴申燕, 胡宇欣, 洪峰
作者信息
  • 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 贵阳 561113

    • 张宇馨（1998—），女，硕士在读，研究方向：环境与人群毒理学

通讯作者:

洪峰，E-mail:
Establishment and verification of risk prediction model for abnormal bone mass of ethnic minority postmenopausal women in Guizhou Province
Yu-xin ZHANG, Cheng CHEN, Shen-yan WU, Yu-xin HU, Feng HONG
Affiliations
  • School of Public Health and Health, Guizhou Medical University, Guiyang, Guizhou 561113, China
出版时间: 2024-03-10 doi: 10.20043/j.cnki.MPM.202310100
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摘要
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目的

了解贵州省侗族和苗族绝经女性骨量异常的流行情况及影响因素，建立列线图预测模型并验证，为贵州省少数民族绝经女性骨量异常的预防提供理论依据。

方法

本研究为横断面研究，从西南区域少数民族聚集地世居自然人群队列研究数据库中抽取3 208名侗族、苗族绝经女性为研究对象；使用超声骨密度仪检测跟骨骨密度。尿液中镉、钴、铜、铬、铁、汞、锰、钼、镍、铅、锶、钒、锌元素的浓度使用电感耦合等离子体质谱仪测定；采用二元logistic回归分析尿液金属、人口学特征、生殖特征、民族特色饮食与骨量异常的关联，并构建骨量异常风险预测模型。

结果

研究共纳入3 208人，检出骨量异常共1 520人，骨量异常检出率为47.38%；年龄（OR=1.098，95%CI：1.083～1.113）、骨折史（OR=0.560，95%CI：0.395～0.794）、绝经年龄（OR=0.965，95%CI：0.949～0.982）、活产次数（OR=0.900，95%CI：0.818～0.990）、是否服用避孕药（OR=0.708，95%CI：0.555～0.901）、总体力活动（OR=0.989，95%CI：0.984～0.994）、尿镉（OR=1.060，95%CI：1.009～1.113）和尿铬（OR=1.004，95%CI：1.001～1.008）是骨量异常的独立影响因素，建立预测绝经女性骨量异常患病风险的预测模型Logit (P)=-2.170+0.093×（年龄）-0.580×（骨折史）-0.035×（绝经年龄）-0.105×（活产次数）-0.346×（是否服用过避孕药）-0.011×（总体力活动）+0.058×（尿镉）+0.004×（尿铬），该模型受试者工作特征曲线下面积为0.722，绘制列线图并进行内部验证，结果提示该列线图模型预测骨量异常发生的校准曲线与实际曲线基本吻合，拟合效果良好。

结论

镉、铬是骨量异常的独立危险因素，表明金属暴露会影响骨量异常的发生发展，骨量异常预测模型及评分列线图均得到了较好的预测能力，为筛查贵州省少数民族绝经女性骨量异常提供了参考。

骨量异常  /  尿金属  /  绝经女性  /  列线图
Objective

To investigate the prevalence and influencing factors of abnormal bone mass in postmenopausal women of Dong and Miao ethnic groups in Guizhou Province, and to establish a nomogram prediction model and verify it, so as to provide theoretical basis for the prevention of abnormal bone mass in ethnic minority postmenopausal women in Guizhou Province.

Methods

This study was a cross-sectional study. In total 3 208 postmenopausal women of Dong and Miao ethnic groups were selected from the cohort study database of natural population of ethnic minorities in southwest China, and ultrasonic bone densitometer was used to measure calcaneal bone mineral density (BMD). The concentrations of cadmium, cobalt, copper, chromium, iron, mercury, manganese, molybdenum, nickel, lead, strontium, vanadium, and zinc in urine were determined by inductively coupled plasma mass spectrometer, and the relationship between urine metal, demographic characteristics, reproductive characteristics, ethnic diet, and BMD was analyzed by binary logistic regression, and the risk prediction model of bone mass abnormality was established.

Results

A total of 3 208 people were included in the study, and a total of 1 520 people with abnormal bone mass were detected. The detection rate of abnormal bone mass was 47.38%. Based on age (OR=1.098, 95%CI: 1.083-1.113),history of bone fracture (OR=0.560, 95%CI: 0.395-0.794), age of menopause (OR=0.965, 95%CI: 0.949-0.982), number of live births (OR=0.900, 95%CI: 0.818-0.990), use of contraceptive (OR=0.708, 95%CI: 0.555-0.901), total physical activity (OR=0.989, 95%CI: 0.984-0.994), urinary cadmium (OR:1.060, 95%CI:1.009-1.113), and urinary chromium (OR=1.004, 95%CI:1.001-1.008) were independent influencing factors of abnormal bone mass. To establish a predictive model Logit (P) for predicting the risk of abnormal bone mass in postmenopausal women: Logit (P) =-2.170+0.093 * (age)-0.580 * (history of bone fracture)-0.035 * (menopausal age)-0.105 * (number of live births)-0.346* (whether or not you have taken contraceptive)-0.011 *(total physical activity) + 0.058 * (urinary cadmium) + 0.004 * (urinary chromium). The area under the receiver operating characteristic curve of the model was 0.722. The nomogram was drawn and verified internally. The results showed that the calibration curve predicted by the model for abnormal bone mass was basically consistent with the actual curve, and the fitting effect was good.

Conclusion

Cadmium and chromium are independent risk factors of abnormal bone mass, indicating that metal exposure can affect the occurrence and development of abnormal bone mass. The prediction model and score diagram of bone mass abnormality have good predictive ability, which provides a reference for screening abnormal bone mass of minority postmenopausal women in Guizhou Province.

Abnormal bone mass  /  Urinary metal  /  Postmenopausal women  /  Nomogram
张宇馨, 陈成, 吴申燕, 胡宇欣, 洪峰. 贵州省少数民族绝经女性骨量异常风险预测模型的建立与验证. 现代预防医学, 2024 , 51 (5) : 829 -835 . DOI: 10.20043/j.cnki.MPM.202310100
Yu-xin ZHANG, Cheng CHEN, Shen-yan WU, Yu-xin HU, Feng HONG. Establishment and verification of risk prediction model for abnormal bone mass of ethnic minority postmenopausal women in Guizhou Province[J]. Modern Preventive Medicine, 2024 , 51 (5) : 829 -835 . DOI: 10.20043/j.cnki.MPM.202310100
正文
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骨量异常包括骨质疏松和骨量减少，骨质疏松症（osteoporosis,OP）是一种以骨量减低、骨组织微结构损坏，导致骨脆性增加、易发生骨折为特征的全身性骨病[1]。而骨量减少（Osteopenia）是骨质疏松的前驱症状。OP可发生于任何年龄，但多见于绝经女性和老年男性[2-3]。其中绝经女性由于雌激素缺乏，造成骨代谢失衡，骨微结构破坏，骨量减少，更容易发生骨质疏松症[4]。除了雌激素水平下降以外，女性生殖特征如初潮年龄、怀孕次数以及绝经年龄等也认为是女性骨密度的影响因素[5-6]。目前越来越多的研究关注到金属元素暴露与骨量异常发病风险间的关系[7]，但研究结果尚不一致，且缺少少数民族人群研究。贵州省侗族、苗族人群在饮食结构上也与其它地区人群有一定差异，当地人群喜食腌制蔬菜、油茶、牛/羊瘪、白/红酸汤、紫血肉等食物，因此本研究基于“西南区域少数民族聚集地世居自然人群队列研究”的基线调查数据[8]，探讨贵州省侗族和苗族绝经女性骨量异常的流行情况及影响因素，并建立列线图预测模型，为贵州省少数民族绝经女性骨量异常的预防提供理论依据。
1　资料与方法
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1.1　研究对象
数据源于2018年7月—2019年6月“西南区域少数民族聚集地世居自然人群队列研究”基线调查，调查了贵州省黔东南、黔南地区30～79岁世居三代侗族、苗族居民。纳入标准：（1）30～79岁世居三代的侗族、苗族绝经女性；（2）自愿参加，同意生物样本采集并签署知情同意书。排除标准：（1）精神性疾患、恶性肿瘤及其他相关疾病；（2）缺乏骨密度、尿液和问卷调查、体格检查等数据。本研究通过四川大学华西医院医学伦理委员会（K2016038）和贵州医科大学附属医院医学伦理委员会（2018［094］）批准。所有研究对象均自愿参加，并签署知情同意书。
1.2　骨密度测定
采用超声骨密度测定仪（QSTEOKJ3OOO型，南京科进实业有限公司），对调查对象足跟部进行骨密度测定。参照WHO根据骨密度所制定的标准：骨量正常为骨密度T值>-1SD，骨量异常为骨密度T值≤-1SD。
1.3　尿液中金属元素测定
采集调查对象清晨中段尿，使用电感耦合等离子体质谱仪(ICP-MS,NEXION2000，美国Perkin Elmer公司)检测尿液中镉、钴、铜、铬、铁、汞、锰、钼、镍、铅、锶、钒、锌浓度，低于检测限（LOD）的样品浓度表示为定量限（LOQ）的一半，通过确定尿液的比重校正最终浓度。
1.4　协变量
本次研究中的变量定义如下：（1）民族分为侗族、苗族；（2）户口类型分为农业户口、非农业户口、统一居民户口；（3）婚姻状态分为已婚/同居、离异/分居、丧偶、从未结婚；（4）教育程度分为低于高中学历、高中及大专学历、本科及以上学历；（5）吸烟：研究对象至今吸烟≥100支定义为目前吸烟者，其他研究对象为从未吸烟者[8]；（6）饮酒：研究对象每周都饮酒并且持续一年以上，定义为经常饮酒者；若研究对象只在特殊场合或饮酒频率不足每周一次，定义为偶尔饮酒者；其他研究对象定义为不饮酒者；（7）总体力活动：通过计算与职业、交通、家务和休闲活动的代谢当量总和，单位MET-h/d；（8）体质指数（body mass index，BMI）：BMI=体重（kg）÷身高的二次方（m2）。体重过低：BMI<18.5 kg/m2；体重正常：18.5 kg/m2≤BMI<24 kg/m2；超重：24 kg/m2≤BMI<28 kg/m2；肥胖：BMI≥28 kg/m2；（9）腰围（WC）：男85.00 cm≤WC<95.00 cm、女80.00cm≤WC<90.00 cm定义为中度肥胖；男WC≥95.00 cm、女WC≥90.00 cm定义为重度肥胖；（10）自报高血压、糖尿病是否患病，是否曾经骨折；（11）民族特色饮食：包括腌制蔬菜、糯米、腌制鱼肉类、腊/熏肉、白酸汤、红酸汤、油茶，计算每月平均食用次数。
1.5　统计学分析
本研究采用SPSS 25.0和R（version 4.2.2）进行统计分析。定量资料不服从正态分布用中位数［M（P25，P75）］进行统计描述，组间比较采用Mann-Whitney U检验；定性资料用频数（百分比）描述，组间比较采用χ2检验。采用二元logistic回归分析影响因素与骨量异常之间的关联，基于pROC包，绘制绝经女性骨量异常发生率预测模型的受试者工作特征曲线（receiver operating characteristic curve,ROC）；将尿液金属进行自然对数转换，并与人口学特征、生殖特征、民族特色饮食食用频率基于survival包和rms包共同构建骨量异常预测模型及评分列线图，并采Bootstrap重复抽样法进行内部验证。检验水准α=0.05。
2　结果
收起
2.1　研究对象基本特征
本研究共纳入3 208人，骨量异常检出率为47.38%（1 520/3 208）。骨量正常组年龄中位数为55.61岁，低于骨量异常组62.66岁，差异具有统计学意义（Z=-17.779，P<0.001）；苗族绝经女性骨量异常检出率为51.26%，高于侗族绝经女性的45.20%，存在民族差异（χ2=10.863，P=0.001）；两组在骨密度T值、婚姻状态、饮酒、高血压患病情况、骨折史、总体力活动、BMI、腰围方面差异存在统计学意义；民族特色饮食方面，两组在腌制鱼/肉类、酸汤（白酸）、牛/羊瘪菜、油茶食用次数（次/月）存在统计学差异。见表1。
2.2　生殖特征与绝经女性骨量异常
服用过避孕药组骨量异常检出率为38.34%，低于未服用过组的48.84%，骨量正常组初潮年龄中位数低于骨量异常组，绝经年龄高于骨量异常组。此外，首次生育年龄、怀孕次数、活产次数在两组间差异均具有统计学意义。见表2。
2.3　研究人群尿液金属浓度分布
研究人群尿液金属浓度如表3所示，两组在镉、钴、铬、铁、锰、钼、镍、铅、钒9种尿液金属浓度间的差异具有统计学意义，且骨量异常组尿液金属浓度中位数均高于骨量正常组。
2.4　贵州省少数民族绝经女性骨量异常预测模型构建
本研究进一步采用二元logistic回归，探讨尿液金属浓度、人口学特征、生殖特征、民族特色饮食与骨量异常患病风险的相关性。结果表明，年龄、骨折史、绝经年龄、活产次数、是否服用过避孕药、总体力活动、尿镉、尿铬与绝经女性骨量异常具有相关性，而民族特色饮食食用频率（次/月）与绝经女性骨量异常无关，见表4。因此，纳入以上变量构建贵州省少数民族绝经女性骨量异常预测模型。
构建模型为Logit (P)=-2.170+0.093×（年龄）-0.580×（骨折史）-0.035×（绝经年龄）-0.105×（活产次数）-0.346×（是否服用过避孕药）-0.011×（总体力活动）+0.058×（尿镉）+0.004×（尿铬）。Omnibus检验结果（χ2=480.346，P<0.001）提示该预测模型具有统计学意义，霍斯默-莱梅肖检验结果（χ2=7.412，P=0.493），表明该模型具有良好的拟合度。并且本研究所得模型AUC为0.722(95%CI：0.704～0.740)（P<0.001），具有较好的预测效能。见图1。
根据本研究人群数据制作的预测骨量异常发生率列线图，图中每个变量不同数值对应不同评分，评分越高代表发生骨量异常的机率越高。对于贵州省少数民族绝经女性来说发生骨量异常发生风险较大的三个因素分别是年龄、绝经年龄、总体力活动，且尿镉、铬浓度越高，骨量异常发生风险越大。见图2。
采用Bbootstrap验证法对该模型进行验证，重抽样1 000次，该列线图模型预测骨量异常发生的校准曲线与实际曲线基本吻合，拟合效果良好，见图3。
3　讨论
收起
本研究以侗族、苗族绝经女性为研究对象，骨量异常检出率为47.38%，其中侗族绝经女性骨量异常检出率为45.20%，苗族绝经女性骨量异常检出率为51.26%，高于全国50岁以上低骨量率[9]，骨量异常患病风险与年龄、尿镉和尿铬均呈正相关；与骨折史、总体力活动、绝经年龄、活产次数、服用避孕药史呈负相关。
镉（Cd）是一种有毒重金属，一般以化合态广泛存在于自然界。主要通过废气、废水和废渣等方式排放入环境中，造成环境污染[10]。其生物半衰期可长达10～30年，暴露后容易在机体内蓄积[11]。骨骼是镉的主要靶器官之一[12]。许多研究表明，镉可以通过引起肾脏尤其是肾小管损伤，导致钙重吸收障碍、维生素D合成减少，继发钙磷代谢异常，最终造成骨组织钙磷丢失、骨量异常[13]；镉还能作用于骨髓间充质干细胞（BMSCs），抑制骨髓间充质干细胞（BMSCs）向成骨细胞分化，并直接引起BMSC细胞凋亡[14]；除此之外，镉可以直接影响破骨细胞的活化，促进骨吸收，诱导成骨细胞损伤和氧化应激，导致DNA损伤、线粒体功能障碍和内质网应激，从而导致细胞凋亡[15]。一项队列研究显示，高UCd组（> 10 μg/g cr）患骨质疏松症的风险是低UCd组（<5 μg/g cr）2.24倍[16]，尿镉是骨质疏松及骨量减少的独立危险因素。铬（Cr）是工业过程中天然存在的重金属，Cr6+与一系列疾病和病理有关，是一种强致癌物[17]，而Cr3+是天然脂质和蛋白质代谢所必需的微量，也是葡萄糖耐量因子的重要组成成分，通过增强胰岛素的作用而发挥功效[18]。体外研究表明，Cr6+通过膜转运蛋白被成骨细胞吸收，并迅速还原为Cr3+，导致活性氧、氧化应激和DNA损伤增加[19]。本研究中发现在贵州省侗族、苗族绝经女性中，镉、铬暴露会导致骨量异常患病风险的增加，但由于本研究采用ICP-MS进行尿铬元素浓度测定时，并不能区分Cr3+和Cr6+，所以可能造成研究结果不同，因此该结果还需后续进一步验证。
女性生殖特征也会影响女性的骨密度，造成骨量减少甚至骨质疏松。Yong He等人[20]研究发现女性的初潮年龄超过17岁以及绝经年龄小于48岁均是女性发生骨质疏松的危险因素，早期绝经（绝经年龄小于45岁）和卵巢早衰（绝经年龄小于40岁）与骨质疏松患病风险增加有关[21]。有研究骨密度随着妊娠次数的增加而减少[22]，但也有研究认为妊娠次数对骨质疏松症的发生发展具有保护作用[23]。目前关于女性生殖特征对骨密度的影响结论尚未统一，本研究发现初潮年龄、绝经年龄、是否服用过避孕药、首次生育年龄、怀孕次数、活产次数在两组间的差异均具有统计学意义，且在二元logistic回归中发现，服用过避孕药、绝经年龄、活产次数与贵州省少数民族绝经女性骨量异常患病风险呈负相关，对于这一结果还需后续研究进一步探讨。
本研究有以下优势，一是本研究获得西南区域少数民族聚集地世居自然人群队列研究数据，且所有数据都是由经过专业培训的调查员收集，在问卷调查、体格检查、实验室检测过程中进行了严格的质量控制，确保了信息的可靠性；二是根据筛选出的变量建立了列线图模型，可以根据每项危险因素的评分预测骨量异常，列线图模型简单易懂，自明性较强，具有较高的实践价值，可据此提前做好预防工作。但本研究也存在一定的局限性。一方面本研究为横断面研究，难以得出影响因素与骨量异常间的因果关系；另一方面本研究仅仅探讨了单金属暴露对骨量异常患病风险的影响，未考虑多金属联合暴露的影响，在后续研究中应进一步探讨多金属共同暴露对骨量异常的影响。
综上所述，贵州省侗族、苗族绝经女性尿镉、尿铬与骨量异常患病风险呈正相关，表明金属暴露会影响骨量异常的发生发展。通过建立骨量异常预测模型和评分列线图，可以较好地预测贵州省少数民族绝经女性患骨量异常的风险，为筛查该人群骨量异常提供了参考依据。
基金
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  • 国家重点研发计划课题(2017YFC0907301)
文献
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2024年第51卷第5期
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doi: 10.20043/j.cnki.MPM.202310100
  • 接收时间：2023-10-10
  • 首发时间：2026-03-19
  • 出版时间：2024-03-10
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  • 收稿日期：2023-10-10
基金
国家重点研发计划课题(2017YFC0907301)
作者信息
    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 贵阳 561113

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2种不同金属材料的力学参数
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Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
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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