Impact of Supplementation and Nutritional Interventions on Pathogenic Processes of Mood Disorders: A Review of the Evidence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it