Causal inference on microbiome-metabolome relations in observational host-microbiome data via in silico in vivo association pattern analyses.

ABSTRACT: Understanding the effects of the microbiome on the host's metabolism is core to enlightening the role of the microbiome in health and disease. Herein, we develop the paradigm of in silico in vivo association pattern analyses, combining microbiome metabolome association studies with in silico constraint-based community modeling. Via theoretical dissection of confounding and causal paths, we show that in silico in vivo association pattern analyses allow for causal inference on microbiome-metabolome relations in observational data. We justify the corresponding theoretical criterion by structural equation modeling of host-microbiome systems, integrating deterministic microbiome community modeling into population statistics approaches. We show the feasibility of our approach on a published mult

SUBMITTER: Hertel J 

PROVIDER: S-EPMC10626217 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature