Microbiome and Metabolome Profiles Associated With Different Types of Short Bowel Syndrome: Implications for Treatment
Bibliographic record
Abstract
BACKGROUND: The gut microbiome and metabolome may significantly influence clinical outcomes in patients with short bowel syndrome (SBS). The study aimed to describe specific metagenomic/metabolomics profiles of different SBS types and to identify possible therapeutic targets. METHODS: Fecal microbiome (FM), volatile organic compounds (VOCs), and bile acid (BA) spectrum were analyzed in parenteral nutrition (PN)-dependent SBS I, SBS II, and PN-independent (non-PN) SBS patients. RESULTS: FM in SBS I, SBS II, and non-PN SBS shared characteristic features (depletion of beneficial anaerobes, high abundance of Lactobacilaceae and Enterobacteriaceae). SBS I patients were characterized by the abundance of oxygen-tolerant microrganisms and depletion of strict anaerobes. Non-PN SBS subjects showed markers of partial FM normalization. FM dysbiosis was translated into VOC and BA profiles characteristic for each SBS cohort. A typical signature of all SBS patients comprised high saturated aldehydes and medium-chain fatty acids and reduced short-chain fatty acid (SCFA) content. Particularly, SBS I and II exhibited low protein metabolism intermediate (indole, p-cresol) content despite the hypothetical presence of relevant metabolism pathways. Distinctive non-PN SBS marker was high phenol content. SBS patients' BA fecal spectrum was enriched by chenodeoxycholic and deoxycholic acids and depleted of lithocholic acid. CONCLUSIONS: Environmental conditions in SBS gut significantly affect FM composition and metabolic activity. The common feature of diverse SBS subjects is the altered VOC/BA profile and the lack of important products of microbial metabolism. Strategies oriented on the microbiome/metabolome reconstitution and targeted delivery of key compounds may represent a promising therapeutic strategy in SBS patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".