Impact of Supplementation and Nutritional Interventions on Pathogenic Processes of Mood Disorders: A Review of the Evidence
Bibliographic record
Abstract
This narrative review was conducted using searches of the PubMed/Medline and Google Scholar databases from inception to November 2019. Clinical trials and relevant articles were identified by cross-referencing major depressive disorder (and/or variants) with the following terms: folate, homocysteine, S-adenosylmethionine (SAMe), L-acetylcarnitine, alpha-lipoic acid, N-acetylcysteine, L-tryptophan, zinc, magnesium, vitamin D, omega-3 fatty acids, coenzyme Q10, and inositol. Manual reviews of references were also performed using article reference lists. Abnormal levels of folate, homocysteine, and SAMe have been shown to be associated with a higher risk of depression. Numerous studies have demonstrated antidepressant activity with L-methylfolate and SAMe supplementation in individuals with depression. Additionally, the amino acids L-acetylcarnitine, alpha-lipoic acid, N-acetylcysteine, and L-tryptophan have been implicated in the development of depression and shown to exert antidepressant effects. Other agents with evidence for improving depressive symptoms include zinc, magnesium, omega-3 fatty acids, and coenzyme Q10. Potential biases and differences in study designs within and amongst the studies and reviews selected may confound results. Augmentation of antidepressant medications with various supplements targeting nutritional and physiological factors can potentiate antidepressant effects. Medical foods, particularly L-methylfolate, and other supplements may play a role in managing depression in patients with inadequate response to antidepressant therapies.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".