酚类化合物暴露与人群血脂异常的关联性研究  

Association between phenolic compound exposure and dyslipidemia in the population

作　　者：宋琪哲 李孜孜 慕迪 王惠君 苏畅 武振宇[1] SONG Qizhe;LI Zizi;MU Di;WANG Huijun;SU Chang;WU Zhenyu(School of Public Health,Fudan University,Shanghai 200032,China;National Institute for Nutrition and Health/Key Laboratory of Trace Element Nutrition of National Health Commission,Chinese Center for Disease Control and Prevention,Beijing 100050,China)

机构地区：[1]复旦大学公共卫生学院,上海200032 [2]中国疾病预防控制中心营养与健康所/国家卫生健康委微量元素与营养重点实验室,北京100050

出　　处：《环境与职业医学》2023年第5期565-570,共6页Journal of Environmental and Occupational Medicine

基　　金：国家重点研发计划项目(2019YFC1605100);国家自然科学基金项目(82173613);上海市卫生健康委科研项目(202140018)。

摘　　要：[背景]酚类化合物可能对人体健康带来不利影响,但目前仅有的相关研究多局限于单个酚类化合物暴露对人体健康的影响,尚缺乏多种常见的酚类化合物联合暴露与人群血脂异常的关联性研究。[目的]采用主成分分析-随机森林(PCA-RF)组合策略探索酚类化合物联合暴露与血脂异常之间的关系。[方法]研究数据来源于“美国国家健康与营养调查(2013—2016)”,选择年龄≥20岁,有完整人口学、生活方式、尿液酚类化合物(双酚A、双酚F、双酚S、三氯卡班、二苯甲酮、三氯生)浓度和血清总胆固醇(TC)、甘油三酯(TG)、高密度脂蛋白胆固醇(HDL-C)、低密度脂蛋白胆固醇(LDL-C)水平检测结果的1301名成年居民作为研究对象。6种尿液酚类化合物浓度使用固相萃取偶联高效液相色谱和串联质谱法测定,血脂检测结果采用酶法测定。采用主成分分析联合随机森林模型组合策略建立模型,首先对6种酚类化合物及12项基本特征指标共18个原始变量进行主成分分析,再分别以血脂异常及其四项评价指标为应变量,以提取出的主成分作为自变量,建立随机森林模型。[结果]PCA-RF分析结果显示:双酚A、双酚F、二苯甲酮可能是研究对象血脂异常的重要影响因素;双酚A、双酚F和三氯生可能是研究对象TC水平的重要影响因素;双酚A、双酚F、三氯卡班和二苯甲酮可能是研究对象TG水平的重要影响因素;双酚A可能是研究对象LDL-C水平的重要影响因素;双酚F和二苯甲酮可能是研究对象HDL-C水平的重要影响因素。[结论]酚类化合物暴露可能是人群血脂异常的重要危险因素,PCA-RF组合方法可有效应用于探索人群酚类化合物暴露与血脂异常的关联性分析。[Background]Phenolic compounds may adversely affect human health,but the current relevant studies are mostly limited to the impact of single phenolic compound exposure on human health,and there is still a lack of studies on the population-based association between combined exposure to multiple common phenolic compounds and dyslipidemia.[Objective]To explore the association of phenolic compound combined exposure and dyslipidemia based on principal component analysis-random forest(PCA-RF)strategy.[Methods]The data were from the National Health and Nutrition Examination Survey(2013–2016).A total of 1301 adult residents aged≥20 years with complete information on demographics and lifestyle,urine phenol concentrations(bisphenol A,bisphenol F,bisphenol S,triclocarban,benzophenone,and triclosan),and serum concentrations of total cholesterol(TC),triglycerides(TG),high-density lipoprotein cholesterol(HDL-C),and low-density lipoprotein cholesterol(LDL-C)were included in this study.The concentrations of six urinary phenolic compounds were determined by solid phase extraction coupled with high performance liquid chromatography and tandem mass spectrometry,and the lipid indicators were determined by enzymatic methods.Principal component analysis combined with random forest model was used for model construction.First,principal component analysis was performed on 18 original variables including 6 phenolic compounds and 12 basic characteristic indicators,and then random forest model was established with dyslipidemia and its four evaluation indicators as dependent variables and the extracted principal components as independent variables,respectively.[Results]The PCA-RF analysis showed that bisphenol A,bisphenol F,and benzophenone may be important factors for dyslipidemia in the study subjects;bisphenol A,bisphenol F,and triclosan may be important factors for TC level in the study subjects;bisphenol A,bisphenol F,triclocarban,and benzophenone may be important factors for TG level in the study subjects;bisphenol A may be an impo

关 键 词：血脂异常 酚类化合物 主成分分析 随机森林 美国国家健康与营养调查 

分 类 号：R1[医药卫生—公共卫生与预防医学]