Department of Cardiothoracic Surgery, Children's Hospital of Nanjing Medical University, Nanjing, 210008, China.
Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166, China.
Environ Res. 2019 Nov;178:108666. doi: 10.1016/j.envres.2019.108666. Epub 2019 Aug 14.
Studies have shown that persistent organic pollutants (POPs) can have various health effects. However, little is known about the effects of multiple chemicals with possible common sources of exposure on walking speed, a proxy index reflecting lower limb neuromuscular function and physical function. We simultaneously applied multiple linear and nonlinear statistical models to explore the complex exposure-response relationship between a mixture of 22 selected POPs and walking speed. A total of 14 polychlorinated biphenyls (PCBs), 3 polychlorinated dibenzo-p-dioxins (PCDDs), and 5 polychlorinated dibenzofurans (PCDFs) were measured in the serum of participants in the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2002. Walking speed was measured during a physical examination. Linear regression (LR), least absolute shrinkage and selection operator (LASSO), and group LASSO were used to evaluate the linearity of mixtures, while restricted cubic spline (RCS) regression, random forest (RF), and Bayesian kernel machine regression (BKMR) models were used to evaluate the nonlinearity of mixtures. Potential confounders were adjusted in the above models. A total of 436 subjects were included in our final analysis. The results of the LR model did not identify any POP exposure that was significantly associated with walking speed. The LASSO results revealed an inverse association of one PCDD congener and two PCDF congeners with walking speed, while the group LASSO analysis identified PCDFs at the exposure level and at the group level. In the RCS analysis, two PCB congeners presented significant overall associations with walking speed. The PCB congener PCB194 showed statistically significant effects on the outcome (P = 0.01) when a permutation-based RF was used. The BKMR analysis suggested that PCBs and PCDFs (probabilities = 0.887 and 0.909, respectively) are potentially associated with walking speed. Complex statistical models, such as RCS regression, RF and BKMR models, can detect the nonlinear and nonadditive relationships between PCBs and walking speed, while LASSO and group LASSO can identify only the linear relationships between PCDFs and walking speed. Fully considering the influence of collinearity in each method during modelling can increase the comprehensiveness and reliability of conclusions in studies of multiple chemicals.
研究表明，持久性有机污染物（POPs）会对健康产生各种影响。然而，对于可能具有共同暴露源的多种化学物质对反映下肢神经肌肉功能和身体功能的行走速度的影响知之甚少。我们同时应用了多种线性和非线性统计模型来探索 22 种选定 POPs 混合物与行走速度之间的复杂暴露-反应关系。在 1999 年至 2002 年期间，对来自国家健康和营养检查调查（NHANES）的参与者的血清中 14 种多氯联苯（PCBs）、3 种多氯二苯并对二恶英（PCDDs）和 5 种多氯二苯并呋喃（PCDFs）进行了测量。在体检期间测量了行走速度。线性回归（LR）、最小绝对收缩和选择算子（LASSO）和组 LASSO 用于评估混合物的线性关系，而限制立方样条（RCS）回归、随机森林（RF）和贝叶斯核机器回归（BKMR）模型用于评估混合物的非线性关系。在上述模型中调整了潜在的混杂因素。我们的最终分析共纳入 436 名受试者。LR 模型的结果没有发现任何与行走速度显著相关的 POP 暴露。LASSO 的结果显示，一种 PCDD 同系物和两种 PCDF 同系物与行走速度呈负相关，而组 LASSO 分析则确定了 PCDF 在暴露水平和组水平上的存在。在 RCS 分析中，两种 PCB 同系物与行走速度呈显著总体关联。当使用基于排列的 RF 时，PCB 同系物 PCB194 对结果具有统计学意义（P=0.01）。BKMR 分析表明，PCBs 和 PCDFs（概率分别为 0.887 和 0.909）可能与行走速度有关。复杂的统计模型，如 RCS 回归、RF 和 BKMR 模型，可以检测到 PCBs 与行走速度之间的非线性和非加性关系，而 LASSO 和组 LASSO 只能识别 PCDFs 与行走速度之间的线性关系。在建模过程中充分考虑每种方法的共线性影响，可以提高多化学物质研究中结论的全面性和可靠性。
