Antidepressant foods: An evidence-based nutrient profiling system for depression
Bibliographic record
Abstract
AIM: To investigate which foods are the most nutrient dense sources of nutrients demonstrated by the scientific literature to play a role in the prevention and promotion of recovery from depressive disorders. METHODS: A systematic literature review was conducted to derive a list of Antidepressant Nutrients from the 34 nutrients known to be essential for humans using level of evidence criteria. Nutritional data was extracted for a subset of foods with a high content of at least 1 Antidepressant Nutrient using a USDA database. These foods were analyzed for Antidepressant Nutrient density resulting in an Antidepressant Food Score (AFS). Plant and animal foods were analyzed separately. RESULTS: Twelve Antidepressant Nutrients relate to the prevention and treatment of depressive disorders: Folate, iron, long-chain omega-3 fatty acids (EPA and DHA), magnesium, potassium, selenium, thiamine, vitamin A, vitamin B6, vitamin B12, vitamin C, and zinc. The highest scoring foods were bivalves such as oysters and mussels, various seafoods, and organ meats for animal foods. The highest scoring plant foods were leafy greens, lettuces, peppers, and cruciferous vegetables. CONCLUSION: The AFS is based on a nutrient profiling system devised to identify foods with the highest nutrient density of nutrients with clinical evidence to support their role in depressive disorders. This list of foods and food categories with the highest density of the 12 Antidepressant Nutrients, the Antidepressant Foods, should be considered by researchers in the design of future intervention studies and clinicians as dietary options to support prevention and recovery from depression disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".