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局——即约束于单位单纯形上的向量数据。这一局限对有效评估多重并发环境暴露与此类结局间关联关系构成了挑战。因此，亟需开发能够更准确评估此类复杂关系的分析方法。本研究通过引入采用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，实现了对成分型结局与环境混合物的联合建模。所提出的狄利克雷-BWQS（DBWQS）方法能够同步估计各暴露混合物成分的混合权重，以及总体暴露混合物指数与各结局成分比例之间的关联关系。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之间的关联）评估了DBWQS回归的性能，并将研究结果与分别考虑环境混合物及各结局成分的分析结果进行了比较。最后，我们开发了R软件包xbwqs以公开本方法（https://github.com/hasdk/xbwqs）。