Dietary patterns and the risk of Alzheimer’s disease in an elderly Iranian population: a case–control study
Bibliographic record
Abstract
BACKGROUND: An increasing number of studies in Western countries have shown that healthy eating patterns have a protective effect against cognitive decline and dementia, however, information about this relationship among non-western populations with different cultural environments is scarce. The present study investigated the association between dietary patterns (DPs) and cognitive function in the Iranian elderly. METHODS: In this case-control study, the data of 290 elderly people in two groups of case and control (Mean age in case: 74.2 ± 8.6, in control: 67.3 ± 7.3 year) were analyzed. Two DPs of healthy and unhealthy were extracted from a 142-item dish-based food frequency questionnaire, and patterns driven by principal components analysis (PCA) of 25 food groups. Multivariate binary logistic regression calculated the odds ratio (OR) of cognitive impairment with adjustment for potential confounding factors. RESULTS: A healthy DP, characterized by high consumption of fruits and vegetables, legumes, and nuts, was related to a decrease in the odds of Alzheimer's disease in Iranian elderly people. Also, moderate adherence to an unhealthy food pattern was associated with an increase in the probability of the disease; however, the association was not statistically significant. CONCLUSION: In this elderly population, a healthy eating pattern was associated with reducing the risk of Alzheimer's disease. Further prospective studies are recommended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".