The Gut Microbiome and Mental Health: What Should We Tell Our Patients?: Le microbiote Intestinal et la Santé Mentale : que Devrions-Nous dire à nos Patients?
Bibliographic record
Abstract
The gut microbiome as a potential therapeutic target for mental illness is a hot topic in psychiatry. Trillions of bacteria reside in the human gut and have been shown to play a crucial role in gut-brain communication through an influence on neural, immune, and endocrine pathways. Patients with various psychiatric disorders including depression, bipolar disorder, schizophrenia, and autism spectrum disorder have been shown to have significant differences in the composition of their gut microbiome. Enhancing beneficial bacteria in the gut, for example, through the use of probiotics, prebiotics, or dietary change, has the potential to improve mood and reduce anxiety in both healthy people and patient groups. Much attention is being given to this subject in the general media, and patients are becoming increasingly interested in the potential to treat mental illness with microbiome-based therapies. It is imperative that those working with people with mental illness are aware of the rationale and current evidence base for such treatment strategies. In this review, we provide an overview of the gut microbiome, what it is, and what it does in relation to gut-brain communication and psychological function. We describe the fundamental principles and basic techniques used in microbiome-gut-brain axis research in an accessible way for a clinician audience. We summarize the current evidence in relation to microbiome-based strategies for various psychiatric disorders and provide some practical advice that can be given to patients seeking to try a probiotic for mental health benefit.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".