Evidence of the Dysbiotic Effect of Psychotropics on Gut Microbiota and Capacity of Probiotics to Alleviate Related Dysbiosis in a Model of the Human Colon
Bibliographic record
Abstract
Growing evidence indicates that non-antibiotic therapeutics significantly impact human health by modulating gut microbiome composition and metabolism. In this study, we investigated the impact of two psychotropic drugs, aripiprazole and (S)-citalopram, on gut microbiome composition and its metabolic activity, as well as the potential of probiotics to attenuate related dysbiosis using an ex vivo model of the human colon. After 48 h of fermentation, the two psychotropics demonstrated distinct modulatory effects on the gut microbiome. Aripiprazole, at the phylum level, significantly decreased the relative abundances of Firmicutes and Actinobacteria, while increasing the proportion of Proteobacteria. Moreover, the families Lachnospiraceae, Lactobacillaceae, and Erysipelotrichaceae were also reduced by aripiprazole treatment compared to the control group. In addition, aripiprazole lowered the levels of butyrate, propionate, and acetate, as measured by gas chromatography (GC). On the other hand, (S)-citalopram increased the alpha diversity of microbial taxa, with no differences observed between groups at the family and genus level. Furthermore, a probiotic combination of Lacticaseibacillus rhamnosus HA-114 and Bifidobacterium longum R0175 alleviated gut microbiome alterations and increased the production of short-chain fatty acids to a similar level as the control. These findings provide compelling evidence that psychotropics modulate the composition and function of the gut microbiome, while the probiotic can mitigate related dysbiosis.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".