Department of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Department of Economics, Management and Statistics, University of Milano-Bicocca, Milan, Italy.
Biom J. 2024 Dec;66(8):e202300270. doi: 10.1002/bimj.202300270.
Data integration of multiple studies can provide enhanced exposure contrast and statistical power to examine associations between environmental exposure mixtures and health outcomes. Extant research has combined populations and identified an overall mixture-outcome association, without accounting for differences across studies. We extended the Bayesian Weighted Quantile Sum (BWQS) regression to a hierarchical framework to analyze mixtures across cohorts. The hierarchical BWQS (HBWQS) approach aggregates sample size of multiple cohorts to calculate an overall mixture index, thereby identifying the most harmful exposure(s) across cohorts; and provides cohort-specific associations between the overall mixture index and the outcome. We showed results from 10 simulated scenarios including four mixture components in three, eight, and ten populations, and two real-case examples on the association between prenatal metal mixture exposure-comprising arsenic, cadmium, and lead-and both gestational age and epigenetic-derived gestational age acceleration metrics. Simulated scenarios showed good empirical coverage and little bias for all HBWQS-estimated parameters. The Watanabe-Akaike information criterion showed a better average performance for the HBWQS regression than the BWQS across scenarios. HBWQS results incorporating cohorts within the national Environmental influences on Child Health Outcomes (ECHO) program from three different sites showed that the environmental mixture was negatively associated with gestational age in a single site. The HBWQS approach facilitates the combination of multiple cohorts and accounts for individual cohort differences in mixture analyses. HBWQS findings can be used to develop regulations, policies, and interventions regarding multiple co-occurring environmental exposures and it will maximize the use of extant publicly available data.
多研究数据的整合可以提供增强的暴露对比和统计能力，以研究环境暴露混合物与健康结果之间的关联。现有研究已经结合了人群，并确定了总体混合物-结果关联，而没有考虑到研究之间的差异。我们将贝叶斯加权分位数总和 (BWQS) 回归扩展到一个分层框架中，以分析队列之间的混合物。分层 BWQS (HBWQS) 方法聚合了多个队列的样本量来计算总体混合物指数，从而确定了队列之间最有害的暴露物；并提供了总体混合物指数与结果之间的队列特异性关联。我们展示了 10 种模拟情况的结果，包括三个、八个和十个人群中的四个混合物成分，以及产前金属混合物暴露-包括砷、镉和铅-与胎龄和表观遗传衍生胎龄加速指标之间关联的两个真实案例。模拟情况表明，对于所有 HBWQS 估计参数，经验覆盖度都很好，偏差很小。Watanabe-Akaike 信息准则表明，HBWQS 回归在所有情况下的平均表现都优于 BWQS。纳入来自三个不同地点的国家环境对儿童健康结果（ECHO）计划内的多个队列的 HBWQS 结果表明，环境混合物与单个地点的胎龄呈负相关。HBWQS 方法促进了多个队列的组合，并在混合物分析中考虑了单个队列的差异。HBWQS 的研究结果可用于制定关于多种共同发生的环境暴露的法规、政策和干预措施，并且将最大限度地利用现有的公开可用数据。
