Protein-Balanced Dietary Habits Benefit Cognitive Function in Japanese Older Adults
Bibliographic record
Abstract
Since daily dietary habits can affect cognitive function, dietary patterns such as the Mediterranean-DASH Intervention for Neurodegenerative Delay diet have been proposed as interventions to slow cognitive decline. However, because dietary habits vary widely among different food cultures, it is necessary to establish dietary pattern intervention methods that are appropriate for each population. Therefore, in this study, the dietary patterns of elderly Japanese individuals were classified using cluster analysis, and their relationship with cognitive function was investigated. We then modeled the dietary patterns and applied them to another cohort of elderly Japanese individuals to determine whether differences in dietary patterns could predict cognitive decline. One hundred and fifty older adults ≥ 65 years of age in the community were recruited. Their daily food intake and cognitive function were measured using the brief-type self-administered diet history questionnaire and Montreal Cognitive Assessment, respectively. K-means cluster analysis identified a high-carbohydrate (HC) dietary pattern with high cereal intake and a protein-balanced (PB) dietary pattern with high intake of legumes, vegetables, seafood, meat, and eggs. Cognitive function was significantly higher in the PB group than in the HC group. Furthermore, to classify the new data into HC and PB patterns, a classification model was created by discriminant analysis using food groups with significantly different intakes among dietary patterns. Next, we recruited 267 new older adults ≥ 65 years of age and measured food intake and cognitive function assessed using the memory performance index score. Individuals with cognitive decline were identified and their detailed cognitive functions were assessed using the neurocognitive index score. Cognitive function was significantly impaired in the HC pattern in both the general elderly and cognitively impaired cohorts. These findings suggest that a dietary pattern of low carbohydrate and high protein intake is associated with good cognitive function in elderly Japanese individuals. Classification by these dietary patterns can predict cognitive reservation in community-dwelling older adults.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 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".