Impulse control disorders (ICDs) are a debilitating non-motor symptom of Parkinson’s disease (PD), often associated with dopaminergic therapy. However, their occurrence in some patients but not others suggests additional biological mechanisms, including the gut microbiome. In this study, we analyzed 191 PD patients (14 with ICDs, 177 without) using 16S rRNA gene sequencing to explore the association between gut microbiota and ICDs. No significant differences were observed in alpha or beta diversity between groups, but several bacterial taxa showed differential abundances. Notably, Methanobrevibacter and Intestinimonas butyriciproducens were enriched in ICD patients. Functional pathway analysis revealed differences in metabolic pathways, including enrichment of xenobiotic degradation and nicotinate metabolism in the ICD group. These findings suggest that specific gut microbial taxa and their associated metabolic functions may contribute to ICDs in PD, highlighting a potential non-dopaminergic mechanism and opening new avenues for microbiome-targeted intervention.

Causal mediation analysis examines the mechanism by which exposure affects outcome via mediators. In contrast to single-mediator scenarios, the presence of multiple ordered mediators introduces complex pathways and corresponding path-specific effects, which are difficult to interpret due to the cross-world counterfactual definition. Path-specific effects also require convoluted and unverifiable assumptions for identification. This article proposes a framework of separable path-specific effects as an extension of the separable effect method to the case of multiple ordered mediators. Compared to the traditional approach, separable path-specific effects can be interpreted as the causal effects of several separated components on the outcome, facilitating a more intuitive understanding of underlying mechanisms. We elucidate the relationship between separable and traditional path-specific effects by demonstrating their equivalence under the individual-level isolation assumptions and identifying both effects under the finest fully randomized causally interpretable structured tree graph (FFRCISTG) model, which inherently makes individual-level isolation assumptions. Moreover, weakening the individual-level isolation assumptions to their population-level counterparts, separable path-specific effects remain identifiable under the FFRCISTG model. Under this causal model, the assumptions for identifying separable path-specific effects can be verified in future experiments, thereby addressing the problem of relying on unverifiable cross-world assumptions in the traditional method. We also discuss how this framework can detect violations of assumptions such as the presence of intermediate confounders and the misspecification of causal order among mediators. In summary, compared to the traditional path-specific effects method, the proposed separable method provides a more verifiable and interpretable approach for causal multiple mediation analysis.