Gut Microbiome Patterns Associated With Treatment Response in Patients With Major Depressive Disorder: Changements du microbiote intestinal associés à la réponse au traitement chez des patients souffrant de trouble dépressif majeur
Bibliographic record
Abstract
Objectives: Compelling animal data exists examining the impact of the gut microbiome on the brain, but work is required to translate these findings in a clinical population. We sought to do this by exploring the effects of antidepressant medications on the gut microbiota, and establishing a baseline Major Depressive Disorder (MDD) gut phenotype. Methods: Participants with a primary diagnosis of MDD (n = 15) who were nonmedicated were recruited and followed over 6 months. Stool samples were collected prior to treatment initiation and 3 and 6 months following treatment. 16S rRNA sequencing was employed in order to analyze the gut microbial community profile. Symptom severity was measured by the Beck Depression Inventory. Alpha diversity metrics revealed no significant difference in the community diversity across any of the time-points. Results: Comparison of within-group versus between-group distances revealed a lack of clustering of samples based on time-point, suggesting no significant change in the microbiota across treatment duration. When analyzed based on treatment response, however, patients in the responder group exhibited greater phylogenetic diversity than non-responders (Mann-Whitney U = 5, p = 0.026). At 3-months, 35 Operational Taxonomic Units (OTUs) were significantly different between groups and at 6-months, 42 OTUs were significantly different between responders and non-responders. Conclusions: These observations indicate that antidepressant medications alter the gut microbiota of patients with MDD, with disparate effects in responders versus non responders. This supports the concept of a microbiota phenotype associate with treatment response in MDD.
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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) | 0.000 | 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) | 0.001 | 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".