Associations Between Health Behaviors, Gastrointestinal Symptoms, and Gut Microbiota in a Cross-Sectional Sample of Cancer Survivors: Secondary Analysis from the Chemo-Gut Study
Bibliographic record
Abstract
Background: Health behaviors, such as diet and exercise, are actions individuals take that can potentially impact gastrointestinal (GI) symptoms and the gut microbiota. Little is known about how health behaviors impact GI symptoms and the gut microbiota after anti-cancer therapies. Methods: This is a secondary analysis of a cross-sectional study that investigated relationships between GI symptoms, gut microbiota, and patient-reported outcomes in adult cancer survivors. Gut microbiota was assessed from stool samples using 16 S rRNA gene sequencing. GI symptoms and health behaviors were measured via self-report. Descriptive statistics, multiple regression, and correlation analyses are reported. Results: A total of 334 cancer survivors participated, and a subsample of 17 provided stool samples. Most survivors rated their diet as moderately healthy (55.7%) and reported engaging in low intensity exercise (53.9%) for ≤5 h/week (69.1%). Antibiotic use was associated with more belly pain, constipation, and diarrhea ( P < .05). Survivors consuming a healthier diet had fewer symptoms of belly pain ( P = .03), gas/bloating ( P = .01), while higher protein consumption was associated with less belly pain ( P = .03). Better diet health was positively correlated with Lachnospiraceae abundance, and negatively with Bacteroides abundance ( P < .05). Greater exercise frequency positively correlated with abundance of Lachnospiraceae, Faecalibacterium, Bacteroides, Anaerostipes, Alistipes, and Subdoligranulum ( P < .05). Conclusion: Results provide evidence for associations between antibiotic use, probiotic use, dietary health behaviors, and GI symptoms. Diet and exercise behaviors are related to certain types of bacteria, but the direction of causality is unknown. Dietary-based interventions may be optimally suited to address survivors’ GI symptoms by influencing the gut microbiota. Larger trials 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".