A role for diet and gut microbiota metabolites in autologous hematopoietic cell transplant recipients
Bibliographic record
Abstract
Introduction:The gut microbiome has an established role in allogeneic hematopoietic cell transplantation (allo-HCT), but not in an auto-HCT setting.We have hypothesized that fecal short-chain fatty acids (SCFA) and urinary 3-indoxyl sulfate (3-IS), which are metabolites derived from the action of the gut microbiome on dietary fiber, play a role in auto-HCT outcomes.Methods: This was a single-center prospective study involving auto-HCT recipients.Baseline patient and disease details, diet diaries, and antibiotic exposure were recorded in consenting patients.Serial (pre-HCT, week two, and week four post-HCT) SCFA and urine 3-IS levels were measured using liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS).HCT outcomes were correlated with these metabolites.Results: Thirty patients (myeloma, n=13; lymphoma, n=17) were analyzed.The levels of urinary 3-IS, fecal acetate, propionate, and butyrate were found to be decreased at week two and were recovered by week four post-HCT.Those with low median nadir fecal butyrate levels at week two also had significantly lower pre-HCT and week four butyrate levels.Recipients with low butyrate levels had more grade ≥2 mucositis (80% vs. 33%, p=0.01) and low fiber intake (10.4 g vs. 13.6 g, p=0.04).They also had more carbapenem exposure (93% vs. 47%, p=0.005) and prolonged antibiotics (11 days vs. 8 days, p=0.008).There were no differences in the time to neutrophil or platelet engraftment, mortality, or disease response.Conclusion: Low pre-HCT fecal butyrate levels tend to persist post-HCT and they are associated with mucositis, dietary fiber intake, and antibiotic exposure.The gut microbiome and its modulation may play a role in auto-HCT settings.
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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 | 0.001 |
| 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) | 0.002 | 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".