Flavonoid intake and disability-adjusted life years due to Alzheimer’s and related dementias: a population-based study involving twenty-three developed countries
Bibliographic record
Abstract
OBJECTIVE: Dietary flavonoids and their metabolites may have neuroprotective effects against age-associated neurodegenerative disorders such as Alzheimer's and related dementias (dementia). There is a lack of population studies, however, on correlations between flavonoid intake and dementia. The main objective of the present study was to analyse such a relationship at a large-scale population level. DESIGN: Based on global data (FAO, WHO), databases were generated for: (i) flavonoid content of foods; (ii) per capita national dietary intakes of flavonoids and other dietary factors; and (iii) disability-adjusted life years - a measure of burden and death - due to dementia. Five major flavonoid subclasses were examined. To minimize influences due to accuracy and reliability of the disease source data, twenty-three developed countries were selected after statistical evaluation. RESULTS: Flavonols and combined flavonoids (all five combined) intakes were the only two parameters with significant (P < 0.05) negative dementia correlations. Multiple linear regression models confirmed this relationship, and excluded confounding from some other dietary and non-dietary factors. Similar analyses with non-dementia, neurological/psychiatric diseases did not yield significant correlations. CONCLUSIONS: At a global level, and in the context of different genetic backgrounds, our results suggest that higher consumption of dietary flavonoids, especially flavonols, is associated with lower population rates of dementia in these countries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".