Cognitive decline, dietary factors and gut–brain interactions
Bibliographic record
Abstract
Cognitive decline in elderly people often derives from the interaction between aging-related changes and age-related diseases and covers a large spectrum of clinical manifestations, from intact cognition through mild cognitive impairment and dementia. Epidemiological evidence supports the hypothesis that modifiable lifestyle-related factors are associated with cognitive decline, opening new avenues for prevention. Diet in particular has become the object of intense research in relation to cognitive aging and neurodegenerative disease. We reviewed the most recent findings in this rapidly expanding field. Some nutrients, such as vitamins and fatty acids, have been studied longer than others, but strong scientific evidence of an association is lacking even for these compounds. Specific dietary patterns, like the Mediterranean diet, may be more beneficial than a high consumption of single nutrients or specific food items. A strong link between vascular risk factors and dementia has been shown, and the association of diet with several vascular and metabolic diseases is well known. Other plausible mechanisms underlying the relationship between diet and cognitive decline, such as inflammation and oxidative stress, have been established. In addition to the traditional etiological pathways, new hypotheses, such as the role of the intestinal microbiome in cognitive function, have been suggested and warrant further investigation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".