Effect of dairy consumption on cognition in older adults: A population-based cohort study
Bibliographic record
Abstract
OBJECTIVE: We aimed to assess the effect on cognitive function of adding dairy (total, fermented, non-fermented, full fat, low fat, and sugary) to the diet and of substituting some food groups for dairy. DESIGN: Secondary analysis of a prospective population-based cohort study. PARTICIPANTS: We analyzed data from 1334 cognitively healthy participants (median age 67 years at baseline) with a mean follow-up of 5.6 years from the CoLaus|PsyColaus cohort in Lausanne, Switzerland. MEASUREMENTS: The participants completed a food frequency questionnaire at baseline and cognitive tests at baseline and at follow-up. Clinical dementia rating was the primary outcome. Subjective cognitive decline, memory, verbal fluency, executive and motor functions were secondary outcomes. METHODS: Our exposure was the consumption of total and 5 sub-types of dairy products (g/d). We used marginal structural models to compute average causal effects of 1) increasing dairy consumption by 100 g/d and 2) substituting 100 g/d of meat, fish, eggs, fruits and vegetables with dairy on the outcomes. We used inverse probability of the treatment and lost to follow-up weighting to account for measured confounding and non-random loss to follow-up. RESULTS: Overall, the effects of adding dairy products to the diet on cognition were negligible and imprecise. No substitution had a substantial and consistent effect on clinical dementia rating. The substitution of fish [11.7% (-3% to 26.5%)] and eggs [18% (2.3%-33.7%)] for dairy products could negatively impact verbal memory and neurolinguistic processes. CONCLUSION: We found no effect of adding dairy to the diet or substituting meat, vegetables or fruit for dairy on cognitive function in this cohort of older adults. The substitution of fish and eggs for dairy could have a negative effect on some secondary outcomes, but more studies modeling food substitutions are needed to confirm these results.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".