Dietary patterns: a novel approach to examine the link between nutrition and cognitive function in older individuals
Bibliographic record
Abstract
Cognitive decline may lead to dementia whose most frequent cause is Alzheimer's disease (AD). Among the many potential risk factors of cognitive decline and AD, diet raises increasing interest. Most studies considered diet in the frame of a single nutrient approach with inconsistent results. A novel approach to examine the link between nutrition and cognitive function is the use of dietary patterns. The aim of the present review was to update and complete the body of knowledge about dietary patterns in relationship with various cognitive outcomes in the elderly. Two approaches can be used: a priori and a posteriori patterns. A priori patterns are defined by the adhesion to a pre-defined healthy diet using a score such as the Mediterranean diet (MeDi) score, the Healthy Eating Index, the Canadian Healthy Eating Index, the French National Nutrition and Health Programme (Programme National Nutrition Santé) Guideline Score (PNNS-GS), the Recommended Food Score (RFS) and Dietary Approaches to Stop Hypertension (DASH). MeDi score, RFS, PNNS-GS and DASH have been associated with lower risks of cognitive impairment, cognitive decline, and dementia or AD. Principal components analysis, reduced rank regression and clustering methods allow the identification of 'healthy' patterns associated with lower risk of cognitive decline. However, some studies did not report any associations with cognitive outcomes and results are discordant especially regarding MeDi and the risk of dementia. Several methodological challenges should be overcome to provide a higher level of evidence supporting the development of nutritional policies to prevent cognitive decline and AD.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".