Gut Emotions - Mechanisms of Action of Probiotics as Novel Therapeutic Targets for Depression and Anxiety Disorders
Bibliographic record
Abstract
A priority clinical and research agenda in mood and anxiety disorders is to identify determinants that influence illness trajectory and outcome. Over the past decade, studies have demonstrated a bidirectional relationship between the gut microbiome and brain function (i.e., the microbiota-gut-brain axis). Probiotic treatments and developmental analysis of the microbiome may provide potential treatments and preventative measures for depressive and anxiety disorders. This systematic literature review aims to identify original studies linking the gut microbiota to major depressive disorder and anxiety disorders. Furthermore, this review searched for original reports focusing on possible therapeutic and preventative effects of probiotics for these debilitating conditions. Accumulating data indicate that the gut microbiota communicates with the CNS through neural, endocrine and immune pathways. Studies in germ-free animals indicate that the microbiota is involved in the regulation of the stress response (e.g., hypothalamic-pituitary-adrenal axis) and in CNS development at critical stages. Probiotics attenuate anxiety and depressive-like behaviors in experimental animal models. Notwithstanding some inconsistencies and methodological limitations across trials, clinical studies suggest that probiotics may mitigate anxiety symptoms. However, future studies should investigate the anxiolytic and antidepressant effects of probiotics in more phenotypically homogeneous populations. In conclusion, the emerging concept of a gut microbiota-brain axis suggests that the modulation of the gut microbiota may provide a novel therapeutic target for the treatment and/or prevention of mood and anxiety disorders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".