Major Depressive Disorder and Gut Microbiota: Role of Physical Exercise
Bibliographic record
Abstract
Major depressive disorder (MDD) has a high prevalence and is a major contributor to the global burden of disease. This psychiatric disorder results from a complex interaction between environmental and genetic factors. In recent years, the role of the gut microbiota in brain health has received particular attention, and compelling evidence has shown that patients suffering from depression have gut dysbiosis. Several studies have reported that gut dysbiosis-induced inflammation may cause and/or contribute to the development of depression through dysregulation of the gut-brain axis. Indeed, as a consequence of gut dysbiosis, neuroinflammatory alterations caused by microglial activation together with impairments in neuroplasticity may contribute to the development of depressive symptoms. The modulation of the gut microbiota has been recognized as a potential therapeutic strategy for the management of MMD. In this regard, physical exercise has been shown to positively change microbiota composition and diversity, and this can underlie, at least in part, its antidepressant effects. Given this, the present review will explore the relationship between physical exercise, gut microbiota and depression, with an emphasis on the potential of physical exercise as a non-invasive strategy for modulating the gut microbiota and, through this, regulating the gut-brain axis and alleviating MDD-related symptoms.
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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) | 0.001 | 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 | 0.001 | 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".