Association between Mediterranean diet and dementia and Alzheimer disease: a systematic review with meta-analysis
Bibliographic record
Abstract
Abstract Background Dementia affects 5–8% of the population aged over 65 years (~50 million worldwide). Several factors are associated with increased risk, including diet. The Mediterranean diet (MedDiet) has shown potential protective effects against several chronic diseases. Aims This systematic review with meta-analysis aim was to assess the association between adherence to the MedDiet and the risk of dementia in the elderly. Methods PRISMA-2020 guidelines were followed. PubMed/MEDLINE and Scopus were searched on 17 July 2023. The Newcastle–Ottawa Scale tool was used to assess the risk of bias. The protocol was pre-registered in PROSPERO (registration number: CRD 42023444368). Heterogeneity was assessed using the I2 test. Publication bias was assessed by visual inspection of the funnel plot and by Egger’s regression asymmetry test. The final effect size was reported as OR or HR, depending on the study design of the included studies. Results Out of 682 records, 21 were included in the analysis. The pooled OR was 0.89 (95% CI = 0.84–0.94) based on 65,955 participants (I2 = 69.94). When only cohort studies were included, HR was 0.84 (95% CI = 0.76–0.94) based on 55,205 participants (I2 = 89.70). When only Alzheimer Disease was considered OR was 0.73 (95% CI = 0.62–0.85) based on 38,292 participants (I2 = 63.85). Discussion Despite the relatively low risk reduction associated with higher adherence to MedDiet among elderly, it should be considered that this population is the most affected. Conclusions Adherence to MedDiet could be an effective non-pharmacological measure to reduce the burden of dementia, even among elderly.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.031 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".