Breath volatile metabolome reveals the impact of dietary fibres on the gut microbiota: Proof of concept in healthy volunteers
Bibliographic record
Abstract
Background Current data suggest that dietary fibre (DF) interaction with the gut microbiota largely contributes to their physiological effects. The bacterial fermentation of DF leads to the production of metabolites, most of them are volatile. This study analyzed the breath volatile metabolites (BVM) profile in healthy individuals ( n =15) prior and after a 3-week intervention with chitin-glucan (CG, 4.5 g/day), an insoluble fermentable DF. Methods The present exploratory study presents the original data related to the secondary outcomes, notably the analysis of BVM. BVM were analyzed throughout the test days -in fasting state and after standardized meals - using selected ion flow tube mass spectrometry (SIFT-MS). BVM production was correlated to the gut microbiota composition (Illumina sequencing, primary outcome), analyzed before and after the intervention. Findings The data reveal that the post-prandial state versus fasting state is a key determinant of BVM fingerprint. Correlation analyses with fecal microbiota spotlighted butyrate-producing bacteria, notably Faecalibacterium, as dominant bacteria involved in butyrate and other BVM expiration. CG intervention promotes interindividual variations of fasting BVM, and decreases or delays the expiration of most exhaled BVM in favor of H 2 expiration, without any consequence on gastrointestinal tolerance. Interpretation Assessing BVM is a non-invasive methodology allowing to analyze the influence of DF intervention on the gut microbiota. Funding FiberTAG project was initiated from a European Joint Programming Initiative "A Healthy Diet for a Healthy Life" (JPI HDHL) and was supported by the Service Public de Wallonie (SPW-EER, convention 1610365, Belgium).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".