Decision-relevant measurement for precision microbiome interventions

Human gut microbiome studies often rely on stool, despite increasing evidence that it incompletely represents the spatially heterogeneous intestinal ecosystem. Whether this loss of spatial detail matters for downstream intervention decisions is largely unresolved. We treat compositional reconstruction and decision preservation as separate objectives. Using 16 matched recovery events from 11 participants, each containing stool and four intended regional capsule measurements, we aggregated amplicon sequence variants into 11 prespecified taxonomic–functional proxy guilds. We evaluated regional-to-stool models by nested leave-one-participant-out validation under complementary compositional geometries. We then assessed decision preservation through three spatial proxy states and measurement-induced decision loss (MIDL) across normalized binary intervention thresholds. Multi-region CLR ridge and Aitchison regional-plus-reference models had the lowest point-estimate Aitchison errors, but evidence that either outperformed the population Aitchison center was not retained after multiplicity correction. Rankings changed under total variation and after guild amalgamation; at the coarser resolution, the primary ordering showed a small, uncertain reversal that was sensitive to zero handling. The model with the lowest compositional error did not have the lowest MIDL-curve calibration error. Population centers closely reproduced the nearly uninformative observed-stool MIDL curves while retaining little individualized regional information. These findings show that reconstruction, decision preservation, and calibration to observed-stool decision loss answer different questions. Evaluation of microbiome measurement strategies should therefore treat compositional geometry, biological representation, and the downstream decision task as parts of the estimand.