Integrating complementary datasets for constructing microbiome-metabolome-disease pathways.

Integrating large microbiome cohorts with smaller metabolome data reveals robust microbiome-metabolome-disease links using a novel multi-reference framework.

• Why it matters: Studying microbiome-metabolome-disease pathways is limited by incomplete data and high-dimensional, compositional microbiome measurements, hindering comprehensive understanding of host health.
• What they did: A high-dimensional structural equation model was developed to integrate microbiome, metabolome, and disease data, employing additive log-ratio transformation, penalized two-stage least squares, and a multi-reference permutation test across datasets.
• The result: Application to inflammatory bowel disease cohorts identified biologically meaningful microbiome-metabolome-disease associations, with the method controlling error rates and improving inference robustness.