职业与健康 ›› 2026, Vol. 42 ›› Issue (6): 733-737.

• 论著 • 上一篇    下一篇

广东省某市产业工人尿汞暴露与非酒精性脂肪性肝病的关系研究

陈琳琳1,2, 张镏琢2, 朱德香2, 王熙智2, 孟晓静1, 张明1,2()   

  1. 1 南方医科大学公共卫生学院广东 广州 510515
    2 深圳市职业病防治院广东 深圳 518020
  • 收稿日期:2025-12-11 修回日期:2025-12-16 出版日期:2026-03-15 发布日期:2026-04-03
  • 通信作者: 张明
  • 作者简介:张明,E-mail:zhangming007@smu.edu.cn
    陈琳琳,女,在读硕士研究生,研究方向为劳动卫生学。
  • 基金资助:
    广东省基础与应用基础研究基金(自然科学基金)项目(2022A1515010436);深圳市科技计划项目(可持续发展专项);深圳市科技计划项目(KCXFZ20201221173602007)

Study on the relationship between urinary mercury exposure and nonalcoholic fatty liver disease among industrial workers in a city of Guangdong Province

CHEN Linlin1,2, ZHANG Liuzhuo2, ZHU Dexiang2, WANG Xizhi2, MENG Xiaojing1, ZHANG Ming1,2()   

  1. 1 Southern Medical UniversityGuangzhouGuangdong 510515, China
    2 Shenzhen Prevention and Treatment Center for Occupational DiseasesShenzhenGuangdong 518020, China
  • Received:2025-12-11 Revised:2025-12-16 Online:2026-03-15 Published:2026-04-03
  • Contact: ZHANG Ming

摘要:

目的 探讨广东省某市产业工人尿汞暴露水平与非酒精性脂肪性肝病(non-alcoholic fatty liver disease,NAFLD)风险之间的关系,为制定针对性职业防护措施和健康管理策略提供科学依据。 方法 选取2024年广东省某市产业工人作为研究对象,通过体检系统获取研究对象的基本信息及医学检查等资料,并对数据进行统计分析。采用限制性立方样条(restricted cubic spline,RCS)模型探讨尿汞暴露与NAFLD风险的非线性和剂量-反应关系。 结果 纳入研究对象共269人,其中NAFLD患者82人,检出率为30.5%。研究对象的尿汞暴露值低于职业生物接触限值。两组在年龄和体质量指数(body mass index,BMI)上差异有统计学意义(P<0.05),且NAFLD组各项生化指标水平均高于对照组(均P<0.05)。Logistic回归分析结果显示,尿汞浓度升高与NAFLD风险增加相关,与低暴露组相比,中等尿汞暴露组的NAFLD风险升高(OR=2.200,95%CI:1.143~4.319,P<0.05)。分层分析显示,年龄30~<40岁人群尿汞浓度升高与NAFLD风险增加相关(OR=2.408,95%CI:1.131~5.578,P<0.05)。RCS结果显示,随着尿汞浓度的升高,NAFLD风险总体呈先下降后上升趋势,且存在显著的非线性关系(P<0.05)。 结论 广东省某市产业工人尿汞暴露与NAFLD风险升高相关,并呈非线性关系,且在30~<40岁人群中更为明显,建议加强对广东省某市产业工人的汞暴露监测与健康管理。

关键词: 汞, 非酒精性脂肪性肝病, 二元Logistic回归, 限制性立方样条, 年龄分层, 剂量-反应关系

Abstract:

Objective To investigate the association between urinary mercury exposure levels and the risk of non-alcoholic fatty liver disease(NAFLD) among enterprise workers in a city of Guargdong Province,providing scientific evidence for developing targeted occupational protection measures and health management strategies. Methods Enterprise workers in Shenzhen in 2024 were selected as the study population,basic information and medical examination data of the research subjects were obtained through the physical examination system,and statistical analysis were conducted accordingly. A restricted cubic spline(RCS) model was used to explore the nonlinear and dose-response relationships between urinary mercury exposure and the risk of NAFLD. Results A total of 269 participants were included in this study,among whom 82 had NAFLD,with a detection rate of 30.5%. The urinary mercury exposure level of the study subjects was below the occupational biological exposure limit. There were statistically significant differences in age and body mass index(BMI) between the two groups(both P<0.05),and the NAFLD group had higher levels of all biochemical indicators compared with the control group(both P<0.05). Logistic regression analysis indicated that elevated urinary mercury concentrations were associated with an increased risk of NAFLD. Compared with the low-exposure group,the moderate urinary mercury exposure group showed an increased risk of NAFLD(OR=2.200,95%CI:1.143-4.319,P<0.05). Stratified analysis revealed that in the population aged 30-<40 years,higher urinary mercury concentrations were associated with an increased risk of NAFLD(OR=2.408,95%CI:1.131-5.578,P<0.05). RCS analysis showed that as urinary mercury concentration increased,the risk of NAFLD initially decreased and then increased,with a significant nonlinear relationship(P<0.05). Conclusion Urinary mercury exposure among industrial workers in a city of Guangdong Province is associated with an increased risk of NAFLD and exhibits a nonlinear relationship,which is more pronounced in the population aged 30-<40 years. It is recommended to strengthen the monitoring of mercury exposure and health management for industrial workers in a city of Guangdong Province.

Key words: Mercury, Non-alcoholic fatty liver disease, Binary Logistic regression, Restricted cubic spline, Age stratification, Dose-response relationship

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