Association between plant-based dietary patterns and cognitive function in middle-aged and older residents of China
Bibliographic record
Abstract
BACKGROUND: Plant-based diets may protect against cognitive impairment; however, observational data have not been consistent. OBJECTIVE: This study aimed to evaluate the association between plant-based dietary patterns and cognitive function. METHODS: The study recruited 937 participants who were asked to complete food frequency questionnaires to assess the quality of their plant-based diets using the overall plant-based diet index (PDI), the healthful PDI (hPDI), and the unhealthful PDI (uPDI). Cognitive function evaluated using the Montreal Cognitive Assessment (MoCA) test. Logistic regression was used to explore the association between plant-based dietary patterns and the prevalence of mild cognitive impairment (MCI), while multiple linear regression was used to analyze the association between plant-based dietary patterns and cognitive scores. RESULTS: The prevalence of MCI was 26% among the 937 participants. There was a significant association between higher uPDI scores and higher odds of MCI, with Quintile 4 compared with Quintile 1 showing an odds ratio of 2.21 (95% confidence interval 1.35, 3.60). Higher uPDI scores were associated with a lower total MoCA score and poorer performance in various cognitive domains. There were no significant associations between the PDI, the hPDI, and cognitive function. Consuming whole grains, nuts, and eggs once a week or more were associated with a lower risk of MCI, whereas frequently consumption of pickled vegetables was associated with an increased risk of MCI. CONCLUSIONS: Unhealthy plant-based diets were associated with cognitive impairment, while whole grains, nuts, and eggs may protect cognitive function; pickled vegetables are associated with cognitive impairment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".