Park Sung Kyun, Tao Yebin, Meeker John D, Harlow Siobán D, Mukherjee Bhramar
Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America; Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America.
Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America.
PLoS One. 2014 Jun 5;9(6):e98632. doi: 10.1371/journal.pone.0098632. eCollection 2014.
A growing body of evidence suggests that environmental pollutants, such as heavy metals, persistent organic pollutants and plasticizers play an important role in the development of chronic diseases. Most epidemiologic studies have examined environmental pollutants individually, but in real life, we are exposed to multi-pollutants and pollution mixtures, not single pollutants. Although multi-pollutant approaches have been recognized recently, challenges exist such as how to estimate the risk of adverse health responses from multi-pollutants. We propose an "Environmental Risk Score (ERS)" as a new simple tool to examine the risk of exposure to multi-pollutants in epidemiologic research.
We examined 134 environmental pollutants in relation to serum lipids (total cholesterol, high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL) and triglycerides) using data from the National Health and Nutrition Examination Survey between 1999 and 2006. Using a two-stage approach, stage-1 for discovery (n = 10818) and stage-2 for validation (n = 4615), we identified 13 associated pollutants for total cholesterol, 9 for HDL, 5 for LDL and 27 for triglycerides with adjustment for sociodemographic factors, body mass index and serum nutrient levels. Using the regression coefficients (weights) from joint analyses of the combined data and exposure concentrations, ERS were computed as a weighted sum of the pollutant levels. We computed ERS for multiple lipid outcomes examined individually (single-phenotype approach) or together (multi-phenotype approach). Although the contributions of ERS to overall risk predictions for lipid outcomes were modest, we found relatively stronger associations between ERS and lipid outcomes than with individual pollutants. The magnitudes of the observed associations for ERS were comparable to or stronger than those for socio-demographic factors or BMI.
This study suggests ERS is a promising tool for characterizing disease risk from multi-pollutant exposures. This new approach supports the need for moving from a single-pollutant to a multi-pollutant framework.
越来越多的证据表明，环境污染物，如重金属、持久性有机污染物和增塑剂，在慢性病的发展中起着重要作用。大多数流行病学研究单独考察环境污染物，但在现实生活中，我们接触的是多种污染物和污染混合物，而非单一污染物。尽管多污染物研究方法最近已得到认可，但仍存在挑战，比如如何评估多种污染物对健康产生不良影响的风险。我们提出一种“环境风险评分（ERS）”，作为一种新的简单工具，用于在流行病学研究中考察接触多种污染物的风险。
我们利用1999年至2006年美国国家健康与营养检查调查的数据，研究了134种环境污染物与血脂（总胆固醇、高密度脂蛋白胆固醇（HDL）、低密度脂蛋白胆固醇（LDL）和甘油三酯）之间的关系。采用两阶段方法，第一阶段用于发现（n = 10818），第二阶段用于验证（n = 4615），我们确定了与总胆固醇相关的13种污染物、与HDL相关的9种污染物、与LDL相关的5种污染物以及与甘油三酯相关的27种污染物，并对社会人口学因素、体重指数和血清营养水平进行了调整。利用合并数据和暴露浓度联合分析得出的回归系数（权重），ERS被计算为污染物水平的加权总和。我们针对单独考察（单表型方法）或一起考察（多表型方法）的多种血脂结果计算了ERS。尽管ERS对血脂结果总体风险预测的贡献不大，但我们发现ERS与血脂结果之间的关联相对比与单一污染物之间的关联更强。ERS观察到的关联强度与社会人口学因素或体重指数的关联强度相当或更强。
本研究表明，ERS是一种用于描述多污染物暴露所致疾病风险的有前景的工具。这种新方法支持从单一污染物框架转向多污染物框架的必要性。
