Nutrition-based interventions for mood disorders
Bibliographic record
Abstract
Introduction: ‘Nutritional Psychiatry’ is an emerging area of research that has great potential as an adjunctive tool for the prevention and treatment of diverse neuropsychiatric disorders. Several nutrition-related aspects, such as obesity, dietary patterns, gut microbiome composition and gut permeability, bioactive food compounds, and nutrients can influence pathways implicated in the pathophysiology of mood disorders.Areas covered: Here, the authors review the current evidence on nutrition–mood interaction and nutrition-based treatments for the two main mood disorders, i.e., major depressive disorder and bipolar disorder.Expert opinion: Consistent evidence from observational studies has pointed out the association between a ‘healthy’ diet, generally characterized by a higher intake of fruits, vegetables, legumes, nuts, whole grains, and good quality sources of protein (i.e. fish and/or seafood), and decreased risk of mood disorders and the parallel association between a ‘Western’ diet pattern and increased risk. However, only a few clinical trials have evaluated the effect of nutritional interventions on the treatment of these conditions. The bidirectional interaction between the brain and the gut, named ‘brain-gut-microbiome axis’ or ‘gut-brain axis’, plays a key role in the link between nutrition and mood disorders. Therefore, nutrition-based strategies for gut microbiota modulation are promising fields in mood 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".