Environmental mixture approaches do not accommodate compositional outcomes, consisting of vectors constrained onto the unit simplex. This limitation poses challenges in effectively evaluating the associations between multiple concurrent environmental exposures and their respective impacts on this type of outcomes. As a result, there is a pressing need for the development of analytical methods that can more accurately assess the complexity of these relationships. Here, we extend the Bayesian weighted quantile sum regression (BWQS) framework for jointly modeling compositional outcomes and environmental mixtures using a Dirichlet distribution with a multinomial logit link function. The proposed approach, named Dirichlet-BWQS (DBWQS), allows for the simultaneous estimation of mixture weights associated with each exposure mixture component as well as the association between the overall exposure mixture index and each outcome proportion. We assess the performance of DBWQS regression on extensive simulated data and a real scenario where we investigate the associations between environmental chemical mixtures and DNA methylation-derived placental cell composition, using publicly available data (GSE75248). We also compare our findings with results considering environmental mixtures and each outcome component. Finally, we developed an R package "xbwqs" where we made our proposed method publicly available (https://github.com/hasdk/xbwqs).

翻译：现有环境混合物分析方法无法处理成分型结果——即受限于单位单纯形的向量数据。这一局限性给有效评估多种共存环境暴露与此类结果间关联带来了挑战。因此，亟需开发能够更精确评估此类复杂关系的分析方法。本研究通过引入具有多项对数链接函数的狄利克雷分布，扩展了贝叶斯加权分位数和回归（BWQS）框架，实现了对成分型结果与环境混合物的联合建模。所提出的方法命名为狄利克雷-BWQS（DBWQS），可同步估计各暴露混合物成分的权重系数，以及总体暴露混合物指数与各结果组分比例的关联关系。我们通过大量模拟数据和真实场景（利用公开数据集GSE75248研究环境化学混合物与DNA甲基化衍生的胎盘细胞组成关联）评估了DBWQS回归的性能，并将分析结果与分别考虑环境混合物及各结果组分的传统方法进行对比。最后，我们开发了R软件包"xbwqs"以实现该方法（https://github.com/hasdk/xbwqs）。