Park Sung Kyun, Zhao Zhangchen, Mukherjee Bhramar
Department of Epidemiology, School of Public Health, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA.
Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
Environ Health. 2017 Sep 26;16(1):102. doi: 10.1186/s12940-017-0310-9.
There is growing concern of health effects of exposure to pollutant mixtures. We initially proposed an Environmental Risk Score (ERS) as a summary measure to examine the risk of exposure to multi-pollutants in epidemiologic research considering only pollutant main effects. We expand the ERS by consideration of pollutant-pollutant interactions using modern machine learning methods. We illustrate the multi-pollutant approaches to predicting a marker of oxidative stress (gamma-glutamyl transferase (GGT)), a common disease pathway linking environmental exposure and numerous health endpoints.
We examined 20 metal biomarkers measured in urine or whole blood from 6 cycles of the National Health and Nutrition Examination Survey (NHANES 2003-2004 to 2013-2014, n = 9664). We randomly split the data evenly into training and testing sets and constructed ERS's of metal mixtures for GGT using adaptive elastic-net with main effects and pairwise interactions (AENET-I), Bayesian additive regression tree (BART), Bayesian kernel machine regression (BKMR), and Super Learner in the training set and evaluated their performances in the testing set. We also evaluated the associations between GGT-ERS and cardiovascular endpoints.
ERS based on AENET-I performed better than other approaches in terms of prediction errors in the testing set. Important metals identified in relation to GGT include cadmium (urine), dimethylarsonic acid, monomethylarsonic acid, cobalt, and barium. All ERS's showed significant associations with systolic and diastolic blood pressure and hypertension. For hypertension, one SD increase in each ERS from AENET-I, BART and SuperLearner were associated with odds ratios of 1.26 (95% CI, 1.15, 1.38), 1.17 (1.09, 1.25), and 1.30 (1.20, 1.40), respectively. ERS's showed non-significant positive associations with mortality outcomes.
ERS is a useful tool for characterizing cumulative risk from pollutant mixtures, with accounting for statistical challenges such as high degrees of correlations and pollutant-pollutant interactions. ERS constructed for an intermediate marker like GGT is predictive of related disease endpoints.
人们越来越关注接触污染物混合物对健康的影响。我们最初提出了一种环境风险评分（ERS），作为一种汇总指标，用于在仅考虑污染物主要效应的流行病学研究中检验接触多种污染物的风险。我们使用现代机器学习方法，通过考虑污染物 - 污染物相互作用来扩展ERS。我们阐述了用于预测氧化应激标志物（γ-谷氨酰转移酶（GGT））的多污染物方法，GGT是连接环境暴露和众多健康终点的常见疾病途径。
我们检查了在国家健康与营养检查调查（NHANES 2003 - 2004年至2013 - 2014年，n = 9664）的6个周期中在尿液或全血中测量的20种金属生物标志物。我们将数据随机均匀地分为训练集和测试集，并在训练集中使用具有主要效应和成对相互作用的自适应弹性网（AENET - I）、贝叶斯加法回归树（BART）、贝叶斯核机器回归（BKMR）和超级学习器构建用于GGT的金属混合物的ERS，并在测试集中评估它们的性能。我们还评估了GGT - ERS与心血管终点之间的关联。
在测试集中，基于AENET - I的ERS在预测误差方面比其他方法表现更好。与GGT相关的重要金属包括镉（尿液）、二甲基胂酸、一甲基胂酸、钴和钡。所有的ERS都与收缩压、舒张压和高血压显示出显著关联。对于高血压，AENET - I、BART和超级学习器的每个ERS增加一个标准差分别与比值比1.26（95% CI，1.15，1.38）、1.17（1.09，1.25）和1.30（1.20，1.40）相关。ERS与死亡率结局显示出非显著的正相关。
ERS是一种用于表征污染物混合物累积风险的有用工具，考虑到了诸如高度相关性和污染物 - 污染物相互作用等统计挑战。为像GGT这样的中间标志物构建的ERS可预测相关的疾病终点。
