メタボロミクスデータにおける高次元媒介分析手法の評価

This study evaluated high-dimensional mediation analysis methods (HIMA by Zheng et al. and HDMA by Gao et al.) and the "Meet-in-the-Middle" (MITM) approach using simulated metabolomics data. Simulations varied in sample size, mediator set size, correlation structure, proportion of true mediators, and mediation effect size (beta). We assessed each method's ability to estimate the total indirect effect (TIE), component indirect effects (CIEs), sensitivity, and specificity. In scenarios with independent metabolites, HIMA and HDMA reliably estimated CIEs, while HDMA provided the most accurate estimate of the TIE. MITM generally underestimated the TIE, and HIMA showed improved TIE estimates with higher mediator effect sizes. In correlated settings, CIE estimation was not feasible due to the lack of identifiable causal contrasts, and all methods underestimated the TIE. Sensitivity declined in low beta, small sample size, and high-dimensional scenarios, though specificity remained high (>90%) across all methods. Findings suggest that HIMA offers the most accurate mediation results but may exclude meaningful features through dimensionality reduction. Therefore, applying parallel mediation approaches, such as MITM and HIMA, and focusing on the overlapping findings would be recommended. These results underscore the need for the development of robust, scalable mediation methods tailored to untargeted metabolomics data.