The Chemo-Gut Pilot Study: Associations between Gut Microbiota, Gastrointestinal Symptoms, and Psychosocial Health Outcomes in a Cross-Sectional Sample of Young Adult Cancer Survivors
Bibliographic record
Abstract
Chemotherapy adversely affects the gut microbiota, inducing dysbiosis, and negatively impacts gastrointestinal (GI) and psychosocial health during treatment, but little is known about the long-term effects or how these factors are related. Methods: This cross-sectional pilot study investigated the effects of chemotherapy on the gut microbiota, GI symptoms, and psychosocial outcomes in cancer survivors aged 18−39 years old, compared to healthy controls. Gut microbial diversity and composition were assessed from stool samples using 16S rRNA gene sequencing. Results: Survivors (n = 17) and healthy controls (n = 18) participated. Mean age at diagnosis was 31 years (±5.3). Mean time off treatment was 16.9 months (±16.4). Survivors had more severe GI symptoms, poorer psychosocial health, and increased relative abundance of Selenomondales, Veilloneliaceae, and Intestinibacter. In survivors, Lachnospiraceae, Ruminococcaceae and Intestinibacter correlated with psychosocial symptoms, while diarrhea correlated positively with Lachnospiraceae. Results are statistically significant. Survivors ≤6 months post-treatment had lower alpha diversity than survivors >6 months post-treatment (p = 0.04) and controls (p = 0.19). Conclusion: This small exploratory study demonstrates potential long-term gut microbial dysbiosis in cancer survivors, which may be associated with psychosocial symptoms. Larger trials concurrently and longitudinally examining gut microbiota, GI symptoms, and psychosocial outcomes are needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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".