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Article(id=1241676531764875341, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241676522256388920, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202405094, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1715097600000, receivedDateStr=2024-05-08, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773968354406, onlineDateStr=2026-03-20, pubDate=1731168000000, pubDateStr=2024-11-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773968354406, onlineIssueDateStr=2026-03-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773968354406, creator=13701087609, updateTime=1773968354406, updator=13701087609, issue=Issue{id=1241676522256388920, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='21', pageStart='3841', pageEnd='4032', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773968352140, creator=13701087609, updateTime=1773968629818, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241677686985249701, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241676522256388920, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241677686985249702, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241676522256388920, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=4015, endPage=4022, ext={EN=ArticleExt(id=1241676532666650779, articleId=1241676531764875341, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Correlation analysis of low concentration heavy metal mixed exposure (cadmium, lead, and mercury) and bone density, columnId=1228016569138213037, journalTitle=Modern Preventive Medicine, columnName=Clinical Medicine and Prevention, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the association between mixed exposure to low concentrations of heavy metals (cadmium, lead, mercury) and bone density, as well as the mediating effects of alkaline phosphatase (ALP), bone-specific alkaline phosphatase (BAP), parathyroid hormone (PT), calcium (Ca), and phosphorus (P).

Methods

A cross-sectional study was conducted based on NHANES survey data, including a total of 1 465 participants. Polynomial logistic regression was used to analyze related risk factors, and Bayesian Kernel Machine Regression (BKMR) was employed to assess the impact of mixed exposure to cadmium, lead, and mercury on bone density. The mediation effects of ALP, BAP, PT, Ca, and P in the relationship between exposure to cadmium (Cd), lead (Pb), mercury (Hg), and bone density were analyzed using the Mediation package in R software (Bootstrap method).

Results

The results of polynomial logistic regression indicated that the risk of bone loss increased at the third quartile (T3) of Cd concentration (OR=1.56, 95%CI: 1.08-2.26); the risk also increased at the second quartile (T2) of Pb concentration (OR=1.49, 95%CI: 1.06-2.08). Conversely, the risk of osteoporosis decreased at T3 of Hg concentration (OR=0.38, 95%CI:0.15-0.94). BKMR model analysis revealed that increased concentrations of cadmium, lead, and mercury were associated with a decrease in bone density scores (T-score), with posterior inclusion probabilities for Cd, Pb, and Hg being 1, 1, and 0.47, respectively. Mediation analysis showed that Cd influenced bone density through the mediating effects of ALP and Ca, with mediation proportions of -32.3% and -64.9%, respectively. Hg affected bone density through the mediating effects of ALP and BAP, with mediation proportions of 7.3% and 61.8%. Pb influenced bone density through the mediating effects of ALP, BAP, and PT, with mediation proportions of 38.9%, 97.6%, and 1.5%, respectively.

Conclusion

There is a negative correlation between low concentrations of mixed exposure of cadmium, lead, and mercury and T-score, and the mediating effects of ALP, BAP, PT, and Ca are statistically significant.

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目的

探索低浓度重金属（镉、铅、汞）混合暴露与骨密度之间的关联，以及碱性磷酸酶（ALP）、骨碱性磷酸酶（BAP）、甲状旁腺激素（PT）、钙（Ca）、磷（P）在其中发挥的中介效应。

方法

以NHANES调查数据为基础进行横断面研究，纳入研究对象共1 465人，使用多项式logistic回归分析相关危险因素，并进一步用贝叶斯核函数（BKMR）分析镉、铅、汞混合暴露对骨密度的影响，采用R软件的Mediation包（bootstrap法）分析ALP、BAP、PT、Ca、P在镉（Cd）、铅（Pb）、汞（Hg）暴露与骨密度的关系中的中介效应。

结果

多项式logistic回归结果显示Cd浓度在第三个三分位数（T3）时骨质减少风险增大（OR=1.56，95%CI：1.08～2.26）；Pb浓度在第二个三分位数（T2）时骨质减少风险增大（OR=1.49，95%CI：1.06～2.08）；Hg浓度在T3时骨质疏松风险降低（OR=0.38，95%CI：0.15～0.94）。BKMR模型分析显示Cd、Pb、Hg混合暴露浓度升高会导致骨密度分数（T-score）降低，Cd、Pb、Hg后验纳入概率分别为1、1、0.47。中介分析显示Cd能通过ALP和Ca的中介效应影响骨密度，中介比例分别为-32.3%、-64.9%。Hg能通过ALP和BAP的中介效应影响骨密度，中介比例分别为7.3%、61.8%。Pb能通过ALP、BAP、PT的中介效应影响骨密度，中介比例分别为38.9%、97.6%、1.5%。

结论

低浓度镉、铅、汞混合暴露浓度与T-score呈负相关，ALP、BAP、PT、Ca的中介效应具有统计学意义。

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宋琴，E-mail:
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许译元（2004—），女，本科在读，研究方向：公共卫生

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Chinese Journal of Child Health Care, 2004, 12(2):109-111.(In Chinese), articleTitle=Influence of Lead on bone metabolism of preschool children, refAbstract=null)], funds=[Fund(id=1241821858023150321, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676531764875341, awardId=2024JCXK01, language=CN, fundingSource=杭州师范大学交叉学科研究项目(2024JCXK01), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241821852448919990, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676531764875341, xref=null, ext=[AuthorCompanyExt(id=1241821852457308599, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676531764875341, companyId=1241821852448919990, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of Preventive Medicine, School of Public Health, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China), AuthorCompanyExt(id=1241821852474085817, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676531764875341, companyId=1241821852448919990, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=杭州师范大学公共卫生学院预防医学系，浙江 杭州 311121)])], figs=[ArticleFig(id=1241821855275881094, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676531764875341, language=EN, label=Figure 1, caption=The effects of mixed exposure to heavy metals (Cadmium, lead, mercury) on T-score in adults analyzed by BKMR model, figureFileSmall=QU7VGlyGGtYECoVfQ+2aEQ==, figureFileBig=Jrw73vI1N821hblZHpxbSw==, tableContent=null), ArticleFig(id=1241821855414293138, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676531764875341, language=CN, label=图1, caption=BKMR模型分析重金属（Cd、Pb、Hg）混合暴露对成年人T-score的影响

注：A为重金属混合物的累积效应；B为重金属的单独暴露效应；C为每种重金属的单变量剂量-反应函数；D为交互效应；模型控制年龄、性别、种族、教育程度、贫困指数、饮酒、吸烟、膳食、BMI等因素。

, figureFileSmall=QU7VGlyGGtYECoVfQ+2aEQ==, figureFileBig=Jrw73vI1N821hblZHpxbSw==, tableContent=null), ArticleFig(id=1241821857033294499, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676531764875341, language=EN, label=Table 1, caption=

Basic demographic characteristics ［n=1 465,（）,n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
特征正常（n=890）骨质减少（n=513）骨质疏松（n=62）χ2值P值
年龄(岁)61.95±9.5664.92±10.1368.92±8.5549.060<0.001
PIR2.61±1.622.57±1.612.24±1.402.2180.330
BMI(kg/m2)30.29±6.0927.21±5.4624.86±4.53121.387<0.001
Cd（μg/dl）1.06±1.471.12±1.191.20±0.8610.0500.007
Pb（μg/dl）1.32±1.241.38±1.111.55±1.446.9840.030
Hg（μg/dl）1.62±2.251.65±2.391.35±2.344.5230.104
能量（kcal）2 235±1 1102 017±9871 832±89921.763<0.001
蛋白质（g）84.07±46.0874.88±38.2363.75±33.0128.353<0.001
碳水化合物（g）251.08±132.72235.02±130.27216.47±126.179.0880.011
总糖（g）107.93±76.97102.46±75.5992.56±78.594.9460.084
膳食纤维（g）16.47±10.8716.58±10.8113.66±8.183.6920.158
总脂肪（g）89.99±50.6480.19±47.7371.36±41.8422.475<0.001
维生素A（μg）609.01±584.16645.86±600.56486.60±402.355.1980.074
Ca（g）920.05±563.53871.72±528.61829.16±503.152.8740.238
维生素D（μg）4.69±6.494.71±6.274.24±4.661.0510.591
性别男636（71.46）261（50.88）20（32.26）84.331<0.001
女254（28.54）252（49.12）42（67.74）
种族墨西哥裔美国人81（9.10）30（5.85）3（4.84）80.924<0.001
其他西班牙裔86（9.66）44（8.58）3（4.84）
非西班牙裔白人373（41.91）301（58.67）46（74.19）
非西班牙裔黑人275（30.90）82（15.98）1（1.61）
其他种族，包括多种族75（8.43）56（10.92）9（14.52）
教育程度9年级以下60（6.74）28（5.46）6（9.68）10.2720.246
9～11年级106（11.91）80（15.59）6（9.68）
高中250（28.09）132（25.73）19（30.65）
大学在读或AA学位308（34.61）159（30.99）19（30.65）
大学毕业及以上166（18.65）114（22.22）12（19.35）
饮酒（次/周）从不220（24.72）142（27.68）25（40.32）17.9930.055
每天137（15.39）79（15.40）6（9.68）
几乎每天307（34.49）153（29.82）23（37.10）
3～4115（12.92）58（11.31）4（6.45）
277（8.65）51（9.94）3（4.84）
134（3.82）30（5.85）1（1.61）
吸烟（根/d）1～2247（27.75）135（26.32）22（35.48）4.6420.326
3～561（6.85）27（5.26）2（3.23）
6～7582（65.39）351（68.42）38（61.29）
), ArticleFig(id=1241821857175900847, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676531764875341, language=CN, label=表1, caption=

人口学基本特征［n=1 465,（）,n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
特征正常（n=890）骨质减少（n=513）骨质疏松（n=62）χ2值P值
年龄(岁)61.95±9.5664.92±10.1368.92±8.5549.060<0.001
PIR2.61±1.622.57±1.612.24±1.402.2180.330
BMI(kg/m2)30.29±6.0927.21±5.4624.86±4.53121.387<0.001
Cd（μg/dl）1.06±1.471.12±1.191.20±0.8610.0500.007
Pb（μg/dl）1.32±1.241.38±1.111.55±1.446.9840.030
Hg（μg/dl）1.62±2.251.65±2.391.35±2.344.5230.104
能量（kcal）2 235±1 1102 017±9871 832±89921.763<0.001
蛋白质（g）84.07±46.0874.88±38.2363.75±33.0128.353<0.001
碳水化合物（g）251.08±132.72235.02±130.27216.47±126.179.0880.011
总糖（g）107.93±76.97102.46±75.5992.56±78.594.9460.084
膳食纤维（g）16.47±10.8716.58±10.8113.66±8.183.6920.158
总脂肪（g）89.99±50.6480.19±47.7371.36±41.8422.475<0.001
维生素A（μg）609.01±584.16645.86±600.56486.60±402.355.1980.074
Ca（g）920.05±563.53871.72±528.61829.16±503.152.8740.238
维生素D（μg）4.69±6.494.71±6.274.24±4.661.0510.591
性别男636（71.46）261（50.88）20（32.26）84.331<0.001
女254（28.54）252（49.12）42（67.74）
种族墨西哥裔美国人81（9.10）30（5.85）3（4.84）80.924<0.001
其他西班牙裔86（9.66）44（8.58）3（4.84）
非西班牙裔白人373（41.91）301（58.67）46（74.19）
非西班牙裔黑人275（30.90）82（15.98）1（1.61）
其他种族，包括多种族75（8.43）56（10.92）9（14.52）
教育程度9年级以下60（6.74）28（5.46）6（9.68）10.2720.246
9～11年级106（11.91）80（15.59）6（9.68）
高中250（28.09）132（25.73）19（30.65）
大学在读或AA学位308（34.61）159（30.99）19（30.65）
大学毕业及以上166（18.65）114（22.22）12（19.35）
饮酒（次/周）从不220（24.72）142（27.68）25（40.32）17.9930.055
每天137（15.39）79（15.40）6（9.68）
几乎每天307（34.49）153（29.82）23（37.10）
3～4115（12.92）58（11.31）4（6.45）
277（8.65）51（9.94）3（4.84）
134（3.82）30（5.85）1（1.61）
吸烟（根/d）1～2247（27.75）135（26.32）22（35.48）4.6420.326
3～561（6.85）27（5.26）2（3.23）
6～7582（65.39）351（68.42）38（61.29）
), ArticleFig(id=1241821857335284408, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676531764875341, language=EN, label=Table 2, caption=

The association between heavy metals (cadmium, lead, mercury) and the risk of osteopenia/osteoporosis analyzed by polynomial logistic regression model (n=1 465)

, figureFileSmall=null, figureFileBig=null, tableContent=
重金属
（n=1465）		血液浓度
（μg/dl）		正常/骨质减少/骨质疏松未校正模型OR值（95%CI）校正模型OR值（95%CI）
骨质减少P值骨质疏松P值骨质减少P值骨质疏松P值
Cd连续变量890/513/621.40（1.06～1.86）0.0192.40（1.24～4.65）0.0091.26（0.86～1.85）0.2361.27（0.47～3.42）0.639
T1（<0.68）298/138/111.00（reference）1.00（reference）1.00（reference）1.00（reference）
T2（0.68～1.03）299/182/221.38（1.05～1.82）0.0232.08（0.99～4.40）0.0551.26（0.90～1.76）0.1731.44（0.59～3.54）0.429
T3（>1.03）297/193/291.51（1.14～1.99）0.0042.86（1.39～5.90）0.0041.56（1.08～2.26）0.0182.05（0.77～5.47）0.150
Ptrend0.0090.0050.0390.203
Pb连续变量890/513/621.46（1.07～2.00）0.0182.52（1.19～5.31）0.0161.30（0.85～1.99）0.2211.97（0.66～5.90）0.225
T1（<0.87）298/140/131.00（reference）1.00（reference）1.00（reference）1.00（reference）
T2（0.87～1.17）300/190/221.46（1.11～1.93）0.0071.94（0.95～3.97）0.0701.49（1.06～2.08）0.0221.96（0.82～4.68）0.128
T3（>1.17）296/183/271.38（1.05～1.82）0.0222.27（1.14～4.5）0.0191.37（0.96～1.95）0.0812.28（0.93～5.58）0.071
Ptrend0.0300.0210.1160.080
Hg连续变量890/513/620.96（0.72～1.27）0.7550.51（0.24～1.07）0.0760.78（0.55～1.11）0.1680.49（0.18～1.28）0.144
T1（<0.82）289/180/261.00（reference）1.00（reference）1.00（reference）1.00（reference）
T2（0.82～1.16）307/169/240.89（0.68～1.16）0.3840.89（0.50～1.59）0.6920.88（0.65～1.19）0.4011.02（0.50～2.07）0.958
T3（>1.16）298/164/120.91（0.70～1.19）0.4980.48（0.24～0.97）0.0410.75（0.54～1.04）0.0860.38（0.15～0.94）0.036
Ptrend0.3770.0370.0660.044
), ArticleFig(id=1241821857494667974, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676531764875341, language=CN, label=表2, caption=

多项式logistic回归模型分析重金属（Cd、Pb、Hg）与骨质减少/骨质疏松风险的相关性（n=1 465）

, figureFileSmall=null, figureFileBig=null, tableContent=
重金属
（n=1465）		血液浓度
（μg/dl）		正常/骨质减少/骨质疏松未校正模型OR值（95%CI）校正模型OR值（95%CI）
骨质减少P值骨质疏松P值骨质减少P值骨质疏松P值
Cd连续变量890/513/621.40（1.06～1.86）0.0192.40（1.24～4.65）0.0091.26（0.86～1.85）0.2361.27（0.47～3.42）0.639
T1（<0.68）298/138/111.00（reference）1.00（reference）1.00（reference）1.00（reference）
T2（0.68～1.03）299/182/221.38（1.05～1.82）0.0232.08（0.99～4.40）0.0551.26（0.90～1.76）0.1731.44（0.59～3.54）0.429
T3（>1.03）297/193/291.51（1.14～1.99）0.0042.86（1.39～5.90）0.0041.56（1.08～2.26）0.0182.05（0.77～5.47）0.150
Ptrend0.0090.0050.0390.203
Pb连续变量890/513/621.46（1.07～2.00）0.0182.52（1.19～5.31）0.0161.30（0.85～1.99）0.2211.97（0.66～5.90）0.225
T1（<0.87）298/140/131.00（reference）1.00（reference）1.00（reference）1.00（reference）
T2（0.87～1.17）300/190/221.46（1.11～1.93）0.0071.94（0.95～3.97）0.0701.49（1.06～2.08）0.0221.96（0.82～4.68）0.128
T3（>1.17）296/183/271.38（1.05～1.82）0.0222.27（1.14～4.5）0.0191.37（0.96～1.95）0.0812.28（0.93～5.58）0.071
Ptrend0.0300.0210.1160.080
Hg连续变量890/513/620.96（0.72～1.27）0.7550.51（0.24～1.07）0.0760.78（0.55～1.11）0.1680.49（0.18～1.28）0.144
T1（<0.82）289/180/261.00（reference）1.00（reference）1.00（reference）1.00（reference）
T2（0.82～1.16）307/169/240.89（0.68～1.16）0.3840.89（0.50～1.59）0.6920.88（0.65～1.19）0.4011.02（0.50～2.07）0.958
T3（>1.16）298/164/120.91（0.70～1.19）0.4980.48（0.24～0.97）0.0410.75（0.54～1.04）0.0860.38（0.15～0.94）0.036
Ptrend0.3770.0370.0660.044
), ArticleFig(id=1241821857599525582, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676531764875341, language=EN, label=Table 3, caption=

Mediating effect (95%CI)

, figureFileSmall=null, figureFileBig=null, tableContent=
重金属中介因子间接效应直接效应中介比例（%）
CdALP-0.007（-0.018～0.000）*0.029（-0.047～0.100）-32.3%(-228.7～205.0)
BAP0.009（-0.002～0.020）0.029（-0.042～0.100）22.8%(-207.0～258.0）
PT0.002（-0.004～0.010）0.014（-0.064～0.090）11.8%(-89.5～108.0)
Ca-0.014（-0.029～0.000）*0.036（-0.039～0.110）-64.9%(-480.3～307.0)
P-0.000（-0.010～0.010）0.022（-0.049～0.100）2.0%(-113.6～119.0)
HgALP-0.005（-0.013～0.000）*-0.068（-0.143～0.000）7.3%(-5.3～48.0)
BAP-0.018（-0.031～-0.010）**-0.011（-0.078～0.050）61.8%(-356.8～439.0)
PT-0.003（-0.008～0.000）-0.095（-0.145～-0.040）***3.0%(-1.8～10.0)
Ca-0.003（-0.009～0.000）-0.071（-0.143～0.000）4.1%(-4.2～25.0)
P0.000（0.003～0.000）-0.074（-0.149～0.000）0.1%(-7.1～10.0)
PbALP0.019（0.004～0.040）**0.030（-0.054～0.110）38.9%(-248.4～370.0)
BAP-0.143（-0.202～-0.090）***-0.004（-0.242～0.220）97.6%(-792.2～827.0)
PT0.018（0.005～0.040）**-0.006（-0.113～0.100）1.5%(-430.0～351.0)
Ca0.005（-0.000～0.010）0.044（-0.035～0.120）10.0%(-55.6～106.0)
P-0.000（-0.008～0.010）0.049（-0.029～0.130）0.5%(-44.3～45.0)
), ArticleFig(id=1241821857750520538, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676531764875341, language=CN, label=表3, caption=

中介效应（95%CI）

, figureFileSmall=null, figureFileBig=null, tableContent=
重金属中介因子间接效应直接效应中介比例（%）
CdALP-0.007（-0.018～0.000）*0.029（-0.047～0.100）-32.3%(-228.7～205.0)
BAP0.009（-0.002～0.020）0.029（-0.042～0.100）22.8%(-207.0～258.0）
PT0.002（-0.004～0.010）0.014（-0.064～0.090）11.8%(-89.5～108.0)
Ca-0.014（-0.029～0.000）*0.036（-0.039～0.110）-64.9%(-480.3～307.0)
P-0.000（-0.010～0.010）0.022（-0.049～0.100）2.0%(-113.6～119.0)
HgALP-0.005（-0.013～0.000）*-0.068（-0.143～0.000）7.3%(-5.3～48.0)
BAP-0.018（-0.031～-0.010）**-0.011（-0.078～0.050）61.8%(-356.8～439.0)
PT-0.003（-0.008～0.000）-0.095（-0.145～-0.040）***3.0%(-1.8～10.0)
Ca-0.003（-0.009～0.000）-0.071（-0.143～0.000）4.1%(-4.2～25.0)
P0.000（0.003～0.000）-0.074（-0.149～0.000）0.1%(-7.1～10.0)
PbALP0.019（0.004～0.040）**0.030（-0.054～0.110）38.9%(-248.4～370.0)
BAP-0.143（-0.202～-0.090）***-0.004（-0.242～0.220）97.6%(-792.2～827.0)
PT0.018（0.005～0.040）**-0.006（-0.113～0.100）1.5%(-430.0～351.0)
Ca0.005（-0.000～0.010）0.044（-0.035～0.120）10.0%(-55.6～106.0)
P-0.000（-0.008～0.010）0.049（-0.029～0.130）0.5%(-44.3～45.0)
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低浓度重金属（镉、铅、汞）混合暴露与骨密度的关联分析
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许译元 , 刘锶施 , 施怡 , 宋琴
现代预防医学 | 临床与预防 2024,51(21): 4015-4022
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现代预防医学 | 临床与预防 2024, 51(21): 4015-4022
低浓度重金属（镉、铅、汞）混合暴露与骨密度的关联分析
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许译元, 刘锶施, 施怡, 宋琴
作者信息
  • 杭州师范大学公共卫生学院预防医学系，浙江 杭州 311121

    • 许译元（2004—），女，本科在读，研究方向：公共卫生

通讯作者:

宋琴，E-mail:
Correlation analysis of low concentration heavy metal mixed exposure (cadmium, lead, and mercury) and bone density
Yi-yuan XU, Si-shi LIU, Yi SHI, Qin SONG
Affiliations
  • Department of Preventive Medicine, School of Public Health, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China
出版时间: 2024-11-10 doi: 10.20043/j.cnki.MPM.202405094
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摘要
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目的

探索低浓度重金属（镉、铅、汞）混合暴露与骨密度之间的关联，以及碱性磷酸酶（ALP）、骨碱性磷酸酶（BAP）、甲状旁腺激素（PT）、钙（Ca）、磷（P）在其中发挥的中介效应。

方法

以NHANES调查数据为基础进行横断面研究，纳入研究对象共1 465人，使用多项式logistic回归分析相关危险因素，并进一步用贝叶斯核函数（BKMR）分析镉、铅、汞混合暴露对骨密度的影响，采用R软件的Mediation包（bootstrap法）分析ALP、BAP、PT、Ca、P在镉（Cd）、铅（Pb）、汞（Hg）暴露与骨密度的关系中的中介效应。

结果

多项式logistic回归结果显示Cd浓度在第三个三分位数（T3）时骨质减少风险增大（OR=1.56，95%CI：1.08～2.26）；Pb浓度在第二个三分位数（T2）时骨质减少风险增大（OR=1.49，95%CI：1.06～2.08）；Hg浓度在T3时骨质疏松风险降低（OR=0.38，95%CI：0.15～0.94）。BKMR模型分析显示Cd、Pb、Hg混合暴露浓度升高会导致骨密度分数（T-score）降低，Cd、Pb、Hg后验纳入概率分别为1、1、0.47。中介分析显示Cd能通过ALP和Ca的中介效应影响骨密度，中介比例分别为-32.3%、-64.9%。Hg能通过ALP和BAP的中介效应影响骨密度，中介比例分别为7.3%、61.8%。Pb能通过ALP、BAP、PT的中介效应影响骨密度，中介比例分别为38.9%、97.6%、1.5%。

结论

低浓度镉、铅、汞混合暴露浓度与T-score呈负相关，ALP、BAP、PT、Ca的中介效应具有统计学意义。

重金属  /  混合暴露  /  骨密度  /  中介效应
Objective

To explore the association between mixed exposure to low concentrations of heavy metals (cadmium, lead, mercury) and bone density, as well as the mediating effects of alkaline phosphatase (ALP), bone-specific alkaline phosphatase (BAP), parathyroid hormone (PT), calcium (Ca), and phosphorus (P).

Methods

A cross-sectional study was conducted based on NHANES survey data, including a total of 1 465 participants. Polynomial logistic regression was used to analyze related risk factors, and Bayesian Kernel Machine Regression (BKMR) was employed to assess the impact of mixed exposure to cadmium, lead, and mercury on bone density. The mediation effects of ALP, BAP, PT, Ca, and P in the relationship between exposure to cadmium (Cd), lead (Pb), mercury (Hg), and bone density were analyzed using the Mediation package in R software (Bootstrap method).

Results

The results of polynomial logistic regression indicated that the risk of bone loss increased at the third quartile (T3) of Cd concentration (OR=1.56, 95%CI: 1.08-2.26); the risk also increased at the second quartile (T2) of Pb concentration (OR=1.49, 95%CI: 1.06-2.08). Conversely, the risk of osteoporosis decreased at T3 of Hg concentration (OR=0.38, 95%CI:0.15-0.94). BKMR model analysis revealed that increased concentrations of cadmium, lead, and mercury were associated with a decrease in bone density scores (T-score), with posterior inclusion probabilities for Cd, Pb, and Hg being 1, 1, and 0.47, respectively. Mediation analysis showed that Cd influenced bone density through the mediating effects of ALP and Ca, with mediation proportions of -32.3% and -64.9%, respectively. Hg affected bone density through the mediating effects of ALP and BAP, with mediation proportions of 7.3% and 61.8%. Pb influenced bone density through the mediating effects of ALP, BAP, and PT, with mediation proportions of 38.9%, 97.6%, and 1.5%, respectively.

Conclusion

There is a negative correlation between low concentrations of mixed exposure of cadmium, lead, and mercury and T-score, and the mediating effects of ALP, BAP, PT, and Ca are statistically significant.

Heavy metals  /  Mixed exposure  /  Bone density  /  Mediating effects
许译元, 刘锶施, 施怡, 宋琴. 低浓度重金属（镉、铅、汞）混合暴露与骨密度的关联分析. 现代预防医学, 2024 , 51 (21) : 4015 -4022 . DOI: 10.20043/j.cnki.MPM.202405094
Yi-yuan XU, Si-shi LIU, Yi SHI, Qin SONG. Correlation analysis of low concentration heavy metal mixed exposure (cadmium, lead, and mercury) and bone density[J]. Modern Preventive Medicine, 2024 , 51 (21) : 4015 -4022 . DOI: 10.20043/j.cnki.MPM.202405094
正文
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骨密度（bone mineral density）是评价骨骼健康程度的常用指标[1]。研究发现，低骨密度是骨质疏松症（osteoporosis，OP）的危险因素，骨密度降低可能导致OP，甚至导致骨折进而严重影响人们的健康和生活质量[1]。同时，低骨密度也是跌倒的危险因素之一，跌倒可能导致骨折和严重软组织损伤甚至死亡等伤害[2]。研究表明，血液中铅（lead，Pb）、镉（cadmium，Cd）和汞（mercury，Hg）水平与骨密度存在负相关[1]。目前，大多数研究都集中在单一金属暴露与骨密度的关系上，但在实际中，多种金属的混合暴露更为常见，因此，研究镉、铅、汞混合暴露下的骨密度变化更具有现实意义。碱性磷酸酶（alkaline phosphatase，ALP）、甲状旁腺激素（parathyroid hormone，PT）、骨碱性磷酸酶（bone alkaline phosphatase，BAP）、钙（calcium，Ca）和磷（phosphorus，P）在骨组织发育中也起着重要的作用[3-4]，分析ALP、PT、BAP、Ca、P在其中发挥的中介作用，有助于阐明Cd、Pb、Hg对骨密度变化的内在机制，以便为临床预防和治疗骨骼疾病提供参考，从而实现精准预防和治疗骨骼疾病，保护骨骼健康。
1　对象与方法
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1.1　研究对象
研究人群来自2001—2006年、2013—2014年、2017—2020年的（National Health and Nutrition Examination Survey，NHANES）数据库。该研究遵循赫尔辛基宣言的原则，所有方案都经过伦理委员会的审核和批准。
1.2　T-score
OP和骨质减少症根据世界卫生组织的标准来划分：正常（T-score≥-1），骨质减少（-1>T-score>-2.5），骨质疏松（T-score≤-2.5）。计算T-score时使用的参考人群数据是NHANES III中的平均股骨颈骨密度（0.858 g/cm2）和标准差（s，0.12），国际临床骨密度测量学会也建议使用此参考标准[5-6]。
BMD参与者：观察到的参与者的骨密度，平均BMD参与：参考人群的平均骨密度（0.858 g/cm2），s参考：参考人群的标准差（0.12）。
1.3　社会人口学数据
筛选血液重金属（Cd、Pb、Hg）、ALP、BAP、PT、Ca、P、性别、年龄、种族、教育程度、家庭贫困收入比（family poverty income ratio，PIR）、饮酒、吸烟、膳食（能量、蛋白质、碳水化合物、总糖、膳食纤维、总脂肪、维生素A、钙、维生素D）、身体质量指数（body mass index，BMI）等变量数据均无缺失的数据，共纳入样本量1 465例。
1.4　统计分析方法
本研究采用R 4.3.1软件进行统计分析，正态分布数据用独立样本t检验进行分析，非正态分布数据使用Kruskal-Wallis检验进行分析，分类数据用χ2检验或Fisher精确检验进行分析。纳入所有协变量后，将血液重金属（Cd、Pb、Hg）浓度进行对数转换，并采用多项式logistic回归模型分析相关危险因素（T-score为因变量，1=正常；2=骨质减少；3=骨质疏松)。血液重金属（Cd、Pb、Hg）浓度为自变量。协变量包括年龄、PIR、BMI、膳食、性别（1=男性；2=女性）、种族（1=墨西哥裔美国人；2=其他西班牙裔；3=非西班牙裔白人；4=非西班牙裔黑人；5=其他种族）、教育程度（1=9年级以下；2=9～11年级；3=高中；4=大学在读或AA学位；5表示大学毕业及以上）、饮酒（0=从不；1=每天；2=几乎每天；3=3～4次/周；4=2次/周；5=1次/周）、吸烟（1=1～2根/d；2=3～5根/d；3=6～7根/d）。混合暴露与骨密度的关联采用贝叶斯核函数回归（Bayesian kernel machine regression，BKMR）模型，BKMR是一种目前广泛应用的分析化合物混合暴露复杂性的新方法，使用100节和10 000次马尔可夫链蒙特卡罗迭代将所有BKMR模型拟合为高斯分布。以ALP、PT、BAP、Ca、P为中介变量，采用R软件的Mediation包（非参数百分位bootstrap法）分析ALP、BAP、PT、Ca、P在Cd、Pb、Hg暴露与骨密度的关系中的中介效应，该方法的思想是直接检验系数乘积ab=0是否成立，先进行bootstrap有放回的重复抽样获得bootstrap样本，用该样本的数据计算路径系数a和b，再计算得到系数乘积ab，如果系数乘积ab的区间不包括0，则表明ab≠0，认为中介效应成立，如果包括0，则认为中介效应不存在。检验水准α=0.05。
2　结果
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2.1　人口学基本特征
本研究最终纳入调查对象1 465人，平均年龄为（63.28±9.89）岁，性别、年龄、种族、BMI、重金属浓度（Cd、Pb）、膳食（能量、蛋白质、碳水化合物、总脂肪）对骨密度的影响均有统计学意义（P<0.05）。教育程度、贫困指数PIR、饮酒、吸烟、Hg、总糖、膳食纤维、维生素A、钙、维生素D对骨密度的影响均无统计学意义（P>0.05）。见表1。
2.2　单个重金属与骨密度的关联分析
多项式logistic回归结果中校正前后两种模型的OR值均显示Cd、Pb是影响骨密度的危险因素，见表2。Cd在校正前后的骨质减少人群中Ptrend均小于0.05，说明Cd浓度升高与骨密度下降的关系有统计学意义；在未校正模型中，Cd在T2和T3时对骨质减少人群以及在T3时对骨质疏松人群均有统计学意义（P<0.05）；在校正模型中，浓度在T3时Cd对骨质减少人群仍然具有统计学意义（P<0.05）。在未校正模型中，Pb在T2和T3时对骨质减少人群以及在T3时对骨质疏松人群均有统计学意义（P<0.05）；在校正模型中，Pb在T2时对骨质减少人群仍然具有统计学意义（P<0.05）。校正前后，Hg在骨质疏松人群中Ptrend均小于0.05，说明Hg浓度升高与骨密度增加的关系有统计学意义；在未校正与校正模型中，Hg在T3时对骨质疏松人群均有统计学意义（P<0.05）。
2.3　重金属混合暴露与骨密度的关联分析
BKMR模型结果显示，当三种金属（Cd、Pb、Hg）的混合浓度升高时，T-score下降，Cd、Pb、Hg混合暴露浓度与T-score呈负相关，见图1A，Cd、Pb、Hg后验纳入概率分别为1、1、0.47，表明Cd和Pb在混合暴露中起到主导作用；当其他金属分别固定在第25、50、75分位数时，Cd和Pb暴露浓度从其第25百分位数增加到第75百分位数时，T-score下降，见图1B；图1C显示Cd、Pb与T-score之间呈现出非线性负相关，Hg与T-score呈倒“U”型关系；图1D显示三种金属的暴露-反应函数的斜率在不同分位数上相似，表明三者之间没有交互作用。
2.4　中介效应
以血液污染物（Cd、Hg、Pb）浓度为自变量，正常/骨质减少/骨质疏松的三分类数据为因变量，ALP、PT、BAP、Ca、P为中介变量，并调整所有混杂因素后进行中介效应分析。结果如表3显示，ALP在Cd、Hg、Pb对骨质减少/骨质疏松的中介效应中具有统计学意义（P<0.05）。BAP在Hg和Pb对骨质减少/骨质疏松的中介效应中具有统计学意义（P<0.05）；Ca在Cd对骨质减少/骨质疏松的中介效应中具有统计学意义（P<0.05）；PT在Pb对骨质减少/骨质疏松的中介效应中具有统计学意义（P<0.05）。P的中介效应未观察到有统计学意义（P>0.05）。
3　讨论
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本研究显示性别、年龄、种族、BMI、重金属浓度（Cd、Pb）以及膳食（能量、蛋白质、碳水化合物、总脂肪）与骨密度降低关系密切，与现有文献报道基本一致[7]。骨质减少/骨质疏松在性别中有显著差异，可能与身体活动水平的不同有关，而男性可能从事更多的体力劳动[8]。骨质减少/骨质疏松人群的BMI、能量、蛋白质、碳水化合物、总脂肪的摄入较正常人群低。既往研究表明高BMI能减少骨质疏松发生，因为超重/肥胖者血液中的瘦素、胰岛素等调节因子水平比BMI正常或消瘦者高，能促进骨形成，防止骨量流失[9]，对于老年人来说，研究表明热量干预降低了髋部骨密度，导致骨质流失[10]，蛋白质摄入有利于骨基质的形成和维护[11]，碳水化合物也是骨骼的重要营养来源之一[12]。脂肪对骨密度的影响大多是消极的，但也可能通过施加机械负荷，促进骨的形成[13]。
多项式logistic回归模型显示Cd和Pb浓度升高会增加骨质减少的风险，Hg浓度升高可能会降低骨质疏松的风险。世界卫生组织建议血Cd含量超过0.5 μg/dl时应采取控制措施[14]，本研究中血Cd平均浓度为0.109 μg/dl，低于上述标准。低浓度Cd能直接作用于骨组织，使骨密度降低[15]，还能破坏细胞骨架等诱导成骨细胞的凋亡[16]。Pb暴露会使骨质量下降，该机制可能是Pb影响间充质细胞分化为成骨细胞[17]。本研究中血Pb平均浓度为1.40 μg/dl，世界卫生组织报告显示，母体血Pb暴露水平<5 μg/dl时会导致胎儿生长缓慢，儿童则会出现智商下降，问题行为发生率增加等问题[18]。动物研究表明，Pb（1.7 μg/dl）能直接降低骨密度和皮质骨宽度，使骨折更易发生，还会导致骨重塑失衡，加速骨形成和矿化过程，形成骨骼质量差[19]。美国国家职业安全与健康研究所（NIOSH）推荐全血汞浓度<25 μg/L[20]，研究中人群血Hg平均浓度为0.162 μg/dl，低于该标准，低浓度Hg暴露可能存在应激保护机制（Hormesis效应）。混杂因素校正前后，骨质疏松人群OR值均小于1，说明在低浓度下Hg可能会降低骨质疏松的患病风险，Cho等[21]的研究也发现Hg（平均浓度0.45 μg/dl）与OP风险降低有关。Tang等[22]的研究表明骨密度在参考值（10 μg/dl）范围内随着Hg含量递增，当Hg浓度大于10 μg/dl时，Hg浓度升高股骨骨密度仍然增加。
Cd、Pb.Hg混合暴露浓度与T-score呈负相关，Cd和Pb在混合暴露中起主导作用,每种金属的单变量剂量-反应函数中，单个重金属Cd和Pb浓度与T-score呈明显的非线性负相关。既往研究同样发现
高浓度的Pb、Cd会使骨密度下降，即使是低浓度的重金属（血液中Cd浓度为0.11 μg/dl）也会增加患OP的风险[7-15]。此外，在小鼠研究中发现Pb会影响调节骨骼功能的激素1,25- (OH)2D3的变化，从而影响骨细胞的水平。Cd会间接影响维生素D3的代谢从而影响骨骼的形成和更新。重金属混合物的毒性会降低钙、磷等影响骨骼强度和密度的营养素的功能[23]。Hg与T-score呈倒“U”非线性关系，既往研究发现在男性青少年中，血Hg与骨密度存在倒“U”型关系，低浓度的Hg有益于男性青少年骨密度，高浓度的Hg则会损害男性青少年的骨骼健康[24]。
本研究的中介效应分析显示，Cd能通过ALP和Ca，Hg能通过ALP和BAP，Pb能通过ALP、BAP、PT的中介作用影响骨密度。ALP能限制钙化抑制剂无极焦磷酸盐的浓度，在骨矿化初始阶段发挥重要作用[25]。BAP含量与骨矿化速率呈负相关，BAP升高标志着骨矿化作用减弱[26]。PT能使骨内血钙离子浓度升高，刺激矿物附着率和骨形成率[4]。有实验表明，Cd会降低ALP的表达[27]，暴露于甲基汞或无机汞的海洋鱼类中金属硫蛋白增加，ALP表达减少[28]，Pb暴露会抑制ALP的表达[29]。Pb可能是通过直接抑制成骨细胞功能，使BAP含量增高，影响骨的正常矿化和骨密度[30]。Ca对骨骼的生长发育具有重要作用，能以羟基磷灰石（负责组织矿化）的形式储存[19]，虽然在Pb和Hg对骨密度的影响中没有观察到Ca的中介效应，但是Ca在Pb和Hg对骨质减少/骨质疏松的影响中间接效应的P值较小，分别为0.064和0.178 4，接近0.05，这可能受样本量大小、阳性样本的比例等其他因素的影响，导致检验效能不足。
本研究仍然存在以下局限性。首先，由于其横断面设计，本研究中使用的测量指标和结果代表短期结果，可能仅捕获参与者的近期暴露情况，无法反映Cd、Pb、Hg对骨密度的长期影响。其次，尽管在模型中控制了一些混杂因素，还可能存在其他残余混杂因素。最后，我们的研究结果没有建立Cd、Pb、Hg暴露与骨质疏松风险之间的因果关系，Cd、Pb、Hg暴露对骨质疏松影响的具体机制仍然不明确，需要进一步的研究来探讨Cd、Pb、Hg对骨密度的因果影响。
基金
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  • 杭州师范大学交叉学科研究项目(2024JCXK01)
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参考文献 引证文献
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2024年第51卷第21期
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doi: 10.20043/j.cnki.MPM.202405094
  • 接收时间：2024-05-08
  • 首发时间：2026-03-20
  • 出版时间：2024-11-10
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  • 收稿日期：2024-05-08
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杭州师范大学交叉学科研究项目(2024JCXK01)
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    杭州师范大学公共卫生学院预防医学系，浙江 杭州 311121

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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
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