Bayesian Variable Selection for High-Dimensional Mediation Analysis: Application to Metabolomics Data in Epidemiological Studies.

This research is motivated by integrated epidemiological and blood biomarker studies, investigating the relationship between long-term adherence to a Mediterranean diet and cardiometabolic health, with plasma metabolomes as potential mediators. Analyzing causal mediation in high-dimensional omics data presents challenges, including complex dependencies among mediators and the need for advanced regularization or Bayesian techniques to ensure stable and interpretable estimation and selection of indirect effects. To this end, we propose a novel Bayesian framework to identify active pathways and estimate indirect effects in high-dimensional mediation analysis. Central to our method is the introduction of a set of priors for the selection indicators in the mediator and outcome models. A Markov random field prior leverages mediator correlations, enhancing power in detecting mediated effects. Sequential subsetting priors encourage simultaneous selection of relevant mediators and their indirect effects, ensuring a more coherent and efficient variable selection framework. Comprehensive simulation studies demonstrate that the proposed method provides superior power in detecting active mediating pathways. We further illustrate the practical utility of the method by applying it to metabolome data from two sub-studies within the Health Professionals Follow-up Study and Nurses' Health Study II, highlighting its effectiveness in a real-data setting.