Breath volatile compounds and conjugated polyunsaturated fatty acids as metabolic biomarkers reflecting the interaction between chitin-glucan and the gut microbiota.
Bibliographic record
Abstract
Abstract Introduction Dietary fibers (DF) are considered as beneficial nutrients for health. Current data suggest that their interaction with the gut microbiota largely contributes to their physiological effects. The FiberTAG project(1) innovates in searching for new biomarkers reflecting the health effect of dietary fibers, including chitin-glucan (CG, extracted from the fungal exoskeleton Aspergillus niger). CG improves metabolic disorders associated with obesity in mice, but its effect on gut microbiota composition and function has never been evaluated in vivo in humans. Materials and Methods CG (KitoZyme, Belgium) was given to healthy volunteers (n = 15), during three weeks (4.5g/day). Volatile Organic Compound (VOC) metabolites released in breath were analyzed using SYFT methodology. Fatty acid (FA) profiling was assessed in stool samples, by gas-liquid chromatography(2). The gut microbiome was analyzed by Illumina sequencing (V5-V6 region of 16S rRNA). Results Three weeks of CG supplementation was well tolerated and lead to changes in the kinetics of breath VOC, especially the short-chain fatty acid, alcohols and alkanes. Fecal vaccenic acid, produced upon the bacterial metabolism of fatty acids, was significantly increased by CG. Several bacterial genera were correlated with breath VOC (i.e. 2 methylbutyric acid and RuminococcaceaeUCG005). Moreover, Roseburia, often presented as a butyrate producer, was positively correlated with the production of a rumenic acid isomer “cis-9,cis-11-18:2”. Discussion We show that breath VOC analysis, a non-invasive methodology, reveals characteristics of microbiota-CG interactions. We also show that CG selectively changes the profile of FA metabolites, in favor of vaccenic acid, another bioactive metabolite produced by Roseburia, prone to act on host physiology. This study will help to establish a set of new biomarkers linking insoluble DF and gut microbiota, with focus on their interest in human health.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".