Total Transit Time and Probiotic Persistence in Healthy Adults: A Pilot Study
Bibliographic record
Abstract
Background/Aims: Motility, stool characteristics, and microbiota composition are expected to modulate probiotics' passage through the gut but their effects on persistence after intake cessation remain uncharacterized. This pilot, open-label study aims at characterizing probiotic fecal detection parameters (onset, persistence, and duration) and their relationship with whole gut transit time (WGTT). Correlations with fecal microbiota composition are also explored. Methods: HA-110). Probiotic intake was flanked by 4-week washout periods, with 18 stool collections throughout the study. WGTT was measured using 80% recovery of radio-opaque markers. Results: Tested strains were detected in feces ~1-2 days after first intake and persistence after intake cessation was not significantly different for R0052, HA-108, and HA-129 (~3-6 days). We identified 3 WGTT subgroups within this population (named Fast, Intermediate, and Slow), which could be classified by machine learning with high accuracy based on differentially abundant taxa. On average, R0175 persisted significantly longer in the intermediate WGTT subgroup (~8.5 days), which was mainly due to 6 of the 13 Intermediate participants for whom R0175 persisted ≥ 15 days. Machine learning classified these 13 participants according to their WGTT cluster (≥ 15 days or < 5 days) with high accuracy, highlighting differentially abundant taxa potentially associated with R0175 persistence. Conclusion: These results support the notion that host-specific parameters such as WGTT and microbiota composition should be considered when designing studies involving probiotics, especially for the optimization of washout duration in crossover studies but also for the definition of enrollment criteria or supplementation regimen in specific populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".