Plant-Based Diets and Cognitive Outcomes: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
While plant-rich dietary patterns like the Mediterranean and MIND diets have been linked to cognitive benefits, the role of predominantly plant-based diets is less understood. This systematic review aimed to evaluate the association between plant-based diets and cognitive outcomes. A literature search was conducted in Medline and Embase using keywords related to plant-based diets (e.g. “vegetarian diet”) and cognitive outcomes (e.g. “dementia”). Studies of any design were eligible. Reviewers independently screened studies, extracted data, and assessed quality using the Newcastle-Ottawa Scale. Meta-analyses were conducted on prospective studies that examined the same dietary exposure and cognitive outcome, using fixed-effects regression models. Twenty-two studies were included, with considerable variability in methodologies and outcomes. Plant-based diets were defined either categorically (e.g., vegetarian vs. non-vegetarian), or using indices of adherence, such as the healthful plant-based diet index (hPDI), with higher scores refleting higher adherence. Two meta-analyses, each based on two high-quality prospective cohort studies, examined associations between plant-based diet indices and cognitive outcomes. For cognitive impairment, pooled ORs (95% CI) for highest vs. lowest quartiles were 0.61 (0.55, 0.68; I 2 =97.1%) for PDI and 0.68 (0.62, 0.75; I 2 =84.3%) for hPDI. For dementia, pooled HRs were 1.03 (0.91, 1.17; I 2 =0%) for PDI, 0.85 (0.75, 0.97; I 2 =0%) for hPDI, and 1.17 (1.03, 1.33; I 2 =60.3%) for unhealthful PDI (uPDI). These findings suggest that dietary patterns emphasizing healthful plant-based foods and limiting less healthful plant foods and animal products are associated with lower odds of cognitive impairment and risk of dementia. However, findings across individual studies were inconsistent, highlighting the need for further high-quality research.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".