Comparative dietary sulfated metabolome analysis reveals unknown metabolic interactions of the gut microbiome and the human host
Bibliographic record
Abstract
The gut microbiome converts dietary compounds that are absorbed in the gastrointestinal tract and further metabolized by the human host. Sulfated metabolites are a major compound class derived from this co-metabolism and have been linked to disease development. In the present multidisciplinary study, we have investigated human urine samples from a dietary intervention study with 22 individuals collected before and after consumption of a polyphenol rich breakfast. These samples were analyzed utilizing our method combining enzymatic metabolite hydrolysis using an arylsulfatase and mass spectrometric metabolomics. Key to this study is the validation of 235 structurally diverse sulfated metabolites. We have identified 48 significantly upregulated metabolites upon dietary intervention including 11 previously unknown sulfated metabolites for this diet. We observed a large variation in subjects based on their potential to sulfate metabolites, which may be the foundation for classification of subjects as high and low sulfate metabolizers in future large cohort studies. The reported sulfatase-based method is a robust tool for the discovery of unknown microbiota-derived metabolites in human samples.
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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 | 0.001 | 0.001 |
| 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".