Decision-relevant measurement for precision microbiome interventions
- 1University of California Berkeley
Abstract
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 and functional proxy guilds. We evaluated models that reconstruct stool from regional measurements 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 models combining regional measurements with a population reference 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 MIDL curves of observed stool 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.
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BibTeX
@misc{aminiandehkordi2026decisionrelevant,
title = {{Decision-relevant measurement for precision microbiome interventions}},
author = {J. Aminian-Dehkordi and M. Mofrad},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.09.758009},
url = {https://www.biorxiv.org/content/10.64898/2026.10.09.758009v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Decision-relevant measurement for precision microbiome interventions
AU - J. Aminian-Dehkordi
AU - M. Mofrad
PY - 2026
DA - 2026/10/09
PB - bioRxiv
DO - 10.64898/2026.10.09.758009
UR - https://www.biorxiv.org/content/10.64898/2026.10.09.758009v1
AB - 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 and functional proxy guilds. We evaluated models that reconstruct stool from regional measurements 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 models combining regional measurements with a population reference 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 MIDL curves of observed stool 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.
ER -