Dietary Patterns Are Associated With Multi-Dimensional Cognitive Functions Among Adults Aged 55 and Older in China
Bibliographic record
Abstract
Background The intake of certain food and nutrients may play a crucial role in cognitive health. However, research on the relationship between dietary patterns and cognitive function is limited. This study aims to investigate the associations between dietary patterns and multi-dimensional cognitive functions, such as global cognitive status and related domain profiles, mild cognitive impairment (MCI), and four major subtypes of Chinese adults. Methods Using the baseline data from the Community-based Cohort Study on Nervous System Diseases (2018–2019), we selected 4,309 Chinese adults aged 55 years and older as subjects with complete diet, cognition, and other related data. We collected food data for the past 12 months with a valid semi-quantitative food frequency questionnaire. Diving 49 food items into 13 subgroups, we used factor analysis to derive the main dietary patterns. We evaluated cognitive functions based on the scores of the Montreal Cognitive Assessment (MoCA) and used quantile regression and multivariable logistic regression to examine the relationship between dietary patterns and cognitive-related outcomes. Results We identified four dietary patterns, explaining 50.11% of the total variance: “meat-preferred” pattern, “plant-preferred” pattern, “eggs- and dairy-preferred” pattern, and “grain-preferred” pattern. After adjusting for all potential confounders, the “meat-preferred” pattern and the “plant-preferred” pattern were associated with higher scores of global cognition and several cognitive domains ( p <0.05), while the “grain-preferred” pattern was associated with lower scores of global cognition (β = −0.36, p <0.05), execution (β = −0.19, p <0.05), visuospatial (β = −0.09, p <0.05), and language (β = −0.05, p <0.05). Adults adhering to the “meat-preferred” pattern and the “plant-preferred” pattern had decreased odds of MCI and some MCI subtypes ( p-trend <0.05); in contrast, those in the top quartiles of the “grain-preferred” pattern had increased odds of MCI [adjusted odds ratio ( AOR ) = 1.34, 95% CI: 1.11–1.63, p-trend = 0.003]. Conclusions Adhering to the “plant-preferred” pattern and the “meat-preferred” pattern may help improve the multi-dimensional cognitive functions; on the contrary, adhering to the “grain-preferred” pattern may worse cognitive health. More prospective studies in this field are needed to strengthen the evidence.
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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 | 0.000 | 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".