Milk, Yogurt, and Cheese Intake Is Positively Associated With Cognitive Executive Functions in Older Adults of the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
BACKGROUND: Dairy products provide essential nutrients such as calcium and vitamins B12 and D, and include bioactive peptides and fermented products, which may be beneficial for cognition, especially in older adults. Yet, few studies of large contemporary cohorts have investigated this relationship using sensitive domain-specific cognitive tests. METHOD: In community-dwelling older adults of the Canadian Longitudinal Study on Aging (2011-2015), we examined cross-sectional associations between total and specific dairy product intake and performance in 3 cognitive domains (executive functions, memory, and psychomotor speed). Cheese, milk, yogurt, regular-fat, low-fat, and fermented dairy product intake frequencies were estimated using a food frequency questionnaire; participants were classified into quartiles. Multivariate analyses of covariance models were applied to estimate differences. RESULTS: In 7 945 participants (65-86 years, 49% women, 97% Caucasian), the mean dairy product intake was 1.9 (1.1) times/d. Total dairy product, cheese, and low-fat dairy product intakes were positively associated with the executive function domain and yogurt intake with the memory domain (all p < .05), independently of important covariates including age, gender, education, and diet quality. Intakes of total dairy product, cheese, and low-fat dairy product were associated with verbal fluency specifically (all p < .05). Participants with a dairy product intake >2.5 times/d had a higher score compared to those consuming less. No associations were found with psychomotor speed. CONCLUSIONS: This large cohort study suggests a specific role for dairy components in executive function phonemic verbal fluency and memory. Dairy product intake, a modifiable factor, may be targeted in cognitive health-promoting interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".