Diet and Depression During Peri- and Post-Menopause: A Scoping Review
Bibliographic record
Abstract
Background/Objectives: While the prevalence of depression increases during the peri- and post-menopausal periods, the potential of diet as both a modifiable risk factor and complementary treatment option has received limited research attention in this population. To address this gap, we conducted a scoping review aiming to map and synthesize the existing literature on diet and depression in peri- and post-menopause. Methods: Studies were identified through Medline, EMBASE, PsycINFO, CENTRAL, Web of Science, and Scopus. After deduplication in Covidence, two reviewers independently screened titles, abstracts, and full texts using predefined eligibility criteria. Data were extracted using standardized forms and presented in tables and figures. Methodological quality was assessed using the Cochrane RoB-2 for intervention studies and NHLBI tools for observational studies. Results: Thirty-eight studies met the inclusion criteria, including 29 observational and 9 interventional studies. Dietary patterns showed the most consistent associations with depressive symptoms, whereas findings for foods, nutrients, and other food components were inconsistent. Most observational studies had a moderate to high risk of bias, while over half of experimental studies were rated as low risk. Conclusions: Although limited by volume and poor methodological quality, existing evidence suggests that healthy diets may be protective against depressive symptoms in peri- and post-menopausal women, while unhealthy diets may increase risk. High-quality cohort studies and clinical trials are needed to guide future research and inform professionals working at the intersection of nutrition, psychiatry, and women’s health. Protocol registration: osf.io/b89r6.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".