贵州省苗族人群尿金属与糖尿病及其相关指标的关联性分析  

Association analysis of urinary metals with diabetes and related indicators in Miao population in Guizhou Province

作　　者：杨倩媛 彭莲 张林源 徐子轩 刘磊磊 聂婵 洪峰[1] YANG Qian-yuan;PENG Lian;ZHANG Lin-yuan;XU Zi-xuan;LIU Lei-lei;NIE Chan;HONG Feng(School of Public Health,the Key Laboratory of Environmental Pollution Monitoring and Disease Control,Ministry of Education,Guizhou Medical University,Guiyang,Guizhou 550025,China)

机构地区：[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,贵州贵阳550025

出　　处：《现代预防医学》2022年第20期3822-3828,3840,共8页Modern Preventive Medicine

基　　金：国家重点研发计划课题(2017YFC0907301);国家自然科学基金(82173566)。

摘　　要：目的探讨苗族人群尿金属暴露与空腹血糖、糖化血红蛋白及糖尿病的关联性。方法通过多阶段分层抽样调查贵州省苗族30~79岁之间的居民共3724人,采用电感耦合等离子体质谱法检测尿液金属(砷、镉、镉、钴、铁、汞、锂、钼、铅、锶、钒和锌)浓度。采用最小二乘法回归、稀疏偏最小二乘法回归分析单金属与多金属暴露和空腹血糖、糖化血红蛋白之间的关联。采用logistic回归分析尿液金属与糖尿病之间的关联并构建糖尿病预测模型。结果在稀疏偏最小二乘法模型中,铬(β:0.328,95%CI:0.207~0.444)、锌(β:0.102,95%CI:0.047~0.175)与空腹血糖呈正相关,铁(β:-0.129,95%CI:-0.196~-0.06)、钼(β:-0.061,95%CI:-0.116~-0.016)、铅(β:-0.053,95%CI:-0.088~-0.013)、钒(β:-0.133,95%CI:-0.197~-0.081)与空腹血糖呈负相关;铬(β:0.252,95%CI:0.165~0.332)、锌(β:0.058,95%CI:0.018~0.108)与糖化血红蛋白呈正相关,铁(β:-0.107,95%CI:-0.155~-0.057)、铅(β:-0.034,95%CI:-0.062~-0.003)、钒(β:-0.095,95%CI:-0.139~-0.053)与糖化血红蛋白呈负相关。基于年龄、甘油三酯、收缩压、肌酐清除率、糖尿病家族史、尿铬、尿铁、尿钒和尿锌的独立危险因素,建立预测糖尿病风险的列线图模型,并对该模型进行验证,该模型受试者工作特征曲线下面积为0.787。结论多金属暴露,尤其是铬、铁、钒、锌,与苗族人群空腹血糖、糖化血红蛋白及糖尿病有关,这支持了金属暴露在糖尿病发生发展中的发挥作用的观点。Objective To investigate the association of urinary metal exposure with fasting blood glucose and glycosylated hemoglobin in Miao population.Methods A total of 3724 Miao residents aged 30 to 79 in Guizhou Province were investigated by multi-stage stratified sampling,and the urine metals concentrations(arsenic,cadmium,cadmium,cobalt,iron,mercury,lithium,molybdenum,lead,strontium,vanadium,and zinc)were detected by inductively coupled plasma mass spectrometry.Least squares regression and sparse partial least squares regression were used to analyze the associations between monometallic and multiple metals exposures with fasting blood glucose and glycosylated hemoglobin.Logistic regression was used to analyze the association between urine metals and diabetes,and a diabetes prediction model was constructed.Results In the sPLS model,chromium(β=0.328,95%CI:0.207 to 0.444)and zinc(β=0.102,95%CI:0.047 to 0.175)were positively correlated with fasting blood glucose,iron(β=-0.129,95%CI:-0.196 to-0.06),molybdenum(β=-0.061,95%CI:-0.116 to-0.016),lead(β=-0.053,95%CI:-0.088 to-0.013),and vanadium(β=-0.133,95%CI:-0.197 to-0.081)were negatively correlated with fasting blood glucose;chromium(β=0.252,95%CI:0.165 to 0.332)and zinc(β=0.058,95%CI:0.018 to 0.108)was positively correlated with glycated hemoglobin,iron(β=-0.107,95%CI:-0.155 to-0.057),lead(β=-0.034,95%CI:-0.062 to-0.003),and vanadium(β=-0.095,95%CI:-0.139 to-0.053)were negatively correlated with glycated hemoglobin.Based on independent risk factors of age,triglycerides,systolic blood pressure,creatinine clearance,family history of diabetes,urinary chromium,urinary iron,urinary vanadium,and urinary zinc,a nomogram model for predicting diabetes risk were established and validated.The area under the receiver operating characteristic curve of this model was 0.787.Conclusion Exposure to multiple metals,especially chromium,iron,vanadium and zinc,is related to fasting blood glucose,glycated hemoglobin and diabetes in the Miao population,which supports the notion that me

关 键 词：尿金属 空腹血糖 糖化血红蛋白 糖尿病 苗族 

分 类 号：R714.256[医药卫生—妇产科学]