From the plate to the brain: associations between dietary patterns and reduced dementia prevalence and white matter lesions in older Japanese adults
Bibliographic record
Abstract
Diet is widely considered essential in dementia, but its association with white matter lesions (WMLs) remains unclear. This cross-sectional study investigated the associations between dietary patterns, dementia, and WMLs in a large, nationwide, multicenter population of older Japanese adults. A total of 8,938 adults (aged ≥ 65; 73 ± 6.3 years old) from the Japan Prospective Studies Collaboration for Aging and Dementia (JPSC-AD) were included. Dietary intake was assessed using a Food Frequency Questionnaire. Principal component analysis was used to derive dietary patterns. A trained Convolutional Neural Network model segmented WMLs from brain MR images. Logistic regression estimated odds ratios (ORs) for dementia by dietary pattern quartiles, while linear regression assessed associations with WML volumes. Five dietary patterns were extracted. A Japanese diet including protein and minerals was significantly associated with lower prevalence of all-cause dementia (OR = 0.56) and Alzheimer's disease (OR = 0.47), and with reduced WML volume (β = - 0.03). Similar directional trends in ORs were observed across study sites. The reverse association with WMLs remained significant among individuals without dementia, reducing the likelihood of reverse causation. A Japanese diet including protein and minerals was associated with lower dementia prevalence and smaller WML volume in older Japanese adults. Drawing on nationwide, large-scale, multicenter data, these findings advance our understanding of dietary patterns in older Japanese adults and provide valuable insights for future intervention studies targeting diet and age-related brain changes.
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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".