Vegetarian diet and the risk of depression, anxiety, and stress symptoms: a systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
Previous studies reported inconsistent findings regarding the consumption of a vegetarian diet with mental health outcomes, specifically depression, anxiety and stress. A systematic review was conducted to summarize the current state of literature regarding our understanding of the association between a vegetarian diet and depression, anxiety and stress. A literature search was completed using Scopus, PubMed, and the Web of Science for relevant articles published prior to July 2020. Prospective cohort and cross-sectional studies conducted on adults reporting risk estimates for the consumption of a vegetarian diet, depression, anxiety, and stress were selected. A fixed effects or a random effects model was performed to pool effect sizes. Results from 13 publications (four cohort studies and nine cross-sectional studies) assessing the relationship between the consumption of a vegetarian diet and depression, anxiety and stress were included. The pooled effect size from 10 studies indicated no association between the consumption of a vegetarian diet and depression (pooled effect size: 1.02, 95% CI: 0.84-1.25, p = 0.817). Further, the pooled effect size from four studies suggests that a vegetarian diet is not associated with anxiety (pooled effect size: 1.09, 95% CI: 0.71-1.68, p = 0.678). Due to insufficient data for stress, we were not able to pool the results. Together, no significant associations were observed between the consumption of a vegetarian diet and depression or anxiety. Future cohort studies are needed to further investigate the effects of a vegetarian diet on these mental health outcomes.
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.
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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".