Abstract：
In environmental epidemiology, measurements of toxicants in biological samples are often used as individual exposure assignments. It is common to obtain only one or a few exposure biomarkers per person and use those measurements to represent each person\'s relevant toxicant exposure for a given health outcome, even though most exposure biomarkers can fluctuate over time. When the timing of the exposure reflected by the biomarker measurement is misaligned with disease development especially if it occurs after the disease outcome, results could be subject to reverse causality or exposure measurement error.
This study aimed to use an approximate Bayesian computation (ABC) method to improve PFOA exposure estimates and characterize the effects of PFOA on preeclampsia in the C8 Studies.
Serum PFOA concentrations were measured in blood samples collected during 2005-2006 in West Virginia and Ohio (the C8 Studies), and residential and water use histories and pregnancy outcomes were obtained from self-reports. Our previous results may have been influenced by the choice of methods for characterizing PFOA exposures. Here we use an ABC method to combine measured PFOA serum concentrations and environmentally modeled PFOA concentrations to reconstruct historical PFOA exposures. We also expanded our previous work by assuming more realistic lognormal distributions for key input parameters in the exposure and pharmacokinetic models.
Compared to using fixed values of model parameters and Monte Carlo simulations, ABC produced similar Spearman correlations between estimated and measured serum PFOA concentrations, yet substantially reduced the mean squared error by over 50%. Based on ABC, compared to previous studies, we found a similar adjusted odds ratio (AOR) for the association between PFOA and preeclampsia.
Bayesian combination of modeled exposure and measured biomarker concentrations can reduce exposure measurement error compared to modeled exposure.
This study aimed to use an approximate Bayesian computation (ABC) method to improve PFOA exposure estimates and characterize the effects of PFOA on preeclampsia in the C8 Studies.
Serum PFOA concentrations were measured in blood samples collected during 2005-2006 in West Virginia and Ohio (the C8 Studies), and residential and water use histories and pregnancy outcomes were obtained from self-reports. Our previous results may have been influenced by the choice of methods for characterizing PFOA exposures. Here we use an ABC method to combine measured PFOA serum concentrations and environmentally modeled PFOA concentrations to reconstruct historical PFOA exposures. We also expanded our previous work by assuming more realistic lognormal distributions for key input parameters in the exposure and pharmacokinetic models.
Compared to using fixed values of model parameters and Monte Carlo simulations, ABC produced similar Spearman correlations between estimated and measured serum PFOA concentrations, yet substantially reduced the mean squared error by over 50%. Based on ABC, compared to previous studies, we found a similar adjusted odds ratio (AOR) for the association between PFOA and preeclampsia.
Bayesian combination of modeled exposure and measured biomarker concentrations can reduce exposure measurement error compared to modeled exposure.
摘要：
在环境流行病学中，生物样品中毒物的测量通常用作个体暴露分配。通常每个人只能获得一个或几个暴露生物标志物，并使用这些测量来代表每个人在给定健康结果下的相关毒物暴露。即使大多数暴露生物标志物会随着时间的推移而波动。当生物标志物测量反映的暴露时间与疾病发展不一致时，尤其是在疾病结果之后发生时，结果可能受到反向因果关系或暴露测量误差的影响。
本研究旨在使用近似贝叶斯计算（ABC）方法来改善PFOA暴露估计，并表征C8研究中PFOA对先兆子痫的影响。
在2005-2006年期间在西弗吉尼亚州和俄亥俄州收集的血液样本中测量了血清PFOA浓度（C8研究），从自我报告中获得居住和用水历史以及妊娠结局。我们先前的结果可能受到表征PFOA暴露的方法选择的影响。在这里，我们使用ABC方法将测得的PFOA血清浓度和环境建模的PFOA浓度相结合，以重建历史PFOA暴露。我们还通过对暴露和药代动力学模型中的关键输入参数假设更现实的对数正态分布来扩展我们以前的工作。
与使用模型参数的固定值和蒙特卡罗模拟相比，ABC在估计和测量的血清PFOA浓度之间产生了类似的Spearman相关性，然而，均方误差大大降低了50%以上。基于ABC,与以前的研究相比，我们发现PFOA和先兆子痫之间的关联具有相似的校正比值比(AOR).
与建模暴露相比，建模暴露和测量的生物标志物浓度的贝叶斯组合可以减少暴露测量误差。
本研究旨在使用近似贝叶斯计算（ABC）方法来改善PFOA暴露估计，并表征C8研究中PFOA对先兆子痫的影响。
在2005-2006年期间在西弗吉尼亚州和俄亥俄州收集的血液样本中测量了血清PFOA浓度（C8研究），从自我报告中获得居住和用水历史以及妊娠结局。我们先前的结果可能受到表征PFOA暴露的方法选择的影响。在这里，我们使用ABC方法将测得的PFOA血清浓度和环境建模的PFOA浓度相结合，以重建历史PFOA暴露。我们还通过对暴露和药代动力学模型中的关键输入参数假设更现实的对数正态分布来扩展我们以前的工作。
与使用模型参数的固定值和蒙特卡罗模拟相比，ABC在估计和测量的血清PFOA浓度之间产生了类似的Spearman相关性，然而，均方误差大大降低了50%以上。基于ABC,与以前的研究相比，我们发现PFOA和先兆子痫之间的关联具有相似的校正比值比(AOR).
与建模暴露相比，建模暴露和测量的生物标志物浓度的贝叶斯组合可以减少暴露测量误差。
