Workshop report: Toward the development of a human whole stool reference material for metabolomic and metagenomic gut microbiome measurements
Bibliographic record
Abstract
INTRODUCTION: To date, there has been little effort to develop standards for metabolome-based gut microbiome measurements despite the significant efforts toward standard development for DNA-based microbiome measurements. OBJECTIVES: The National Institute of Standards and Technology (NIST), The BioCollective (TBC), and the North America Branch of the International Life Sciences Institute (ILSI North America) are collaborating to extend NIST's efforts to develop a Human Whole Stool Reference Material for the purpose of method harmonization and eventual quality control. METHODS: The reference material will be rationally designed for adequate quality assurance and quality control (QA/QC) for underlying measurements in the study of the impact of diet and nutrition on functional aspects of the host gut microbiome and relationships of those functions to health. To identify which metabolites deserve priority in their value assignment, NIST, TBC, and ILSI North America jointly conducted a workshop on September 12, 2019 at the NIST campus in Gaithersburg, Maryland. The objective of the workshop was to identify metabolites for which evidence indicates relevance to health and disease and to decide on the appropriate course of action to develop a fit-for-purpose reference material. RESULTS: This document represents the consensus opinions of workshop participants and co-authors of this manuscript, and provides additional supporting information. In addition to developing general criteria for metabolite selection and a preliminary list of proposed metabolites, this paper describes some of the strengths and limitations of this initiative given the current state of microbiome research. CONCLUSIONS: Given the rapidly evolving nature of gut microbiome science and the current state of knowledge, an RM (as opposed to a CRM) measured for multiple metabolites is appropriate at this stage. As the science evolves, the RM can evolve to match the needs of the research community. Ultimately, the stool RM may exist in sequential versions. Beneficial to this evolution will be a clear line of communication between NIST and the stakeholder community to ensure alignment with current scientific understanding and community needs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.091 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.016 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".