Whole Dietary Patterns, Cognitive Decline and Cognitive Disorders: A Systematic Review of Prospective and Intervention Studies
Bibliographic record
Abstract
Dementia prevalence is a global public health concern. Adherence towards a healthy dietary pattern (DP) may reduce the risk of cognitive decline and dementia. This narrative systematic review aimed to synthesise prospective and intervention study data to evaluate the impact of a-posteriori and a-priori derived DPs on cognitive ageing, from cognitive decline to incident dementia. Ninety-three studies were included: 83 prospective studies and 10 randomised controlled trials (RCT). Most prospective studies (77%) examined a-priori DPs, with the Mediterranean diet examined most frequently. A total of 52% of prospective and 50% of RCTs reported a protective relationship between ‘healthy’ DPs and global cognitive decline. Overall, 59% of prospective studies reported positive associations between healthy DPs and risk of cognitive disorder. Incident cognitive disorder was examined by only one intervention study (subgroup analysis) which reported a beneficial effect of a low-fat diet on risk of probable dementia in women. Unhealthy DPs were examined less frequently (n = 17; 21%), with 41% of these studies reporting associations between adherence and poorer cognitive outcomes. Overall, there were mixed results for healthy and unhealthy DPs on cognition, likely due to between-study heterogeneity. Standardisation of diet exposure and cognitive outcome measurement would help to reduce this. Future research would benefit from investigating effects of culturally appropriate DPs on individual cognitive domains and incident cognitive disorders in diverse and high-risk populations.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".