INFLAMMATORY DIET PATTERN AND COGNITIVE FUNCTION IN 5 EUROPEAN COUNTRIES OVER 3-YEARS FOLLOW-UP
Bibliographic record
Abstract
Abstract Diet patterns associated with low chronic inflammation may modulate cognitive decline. We investigated an empirical dietary pattern (EDP) associated with inflammation in five European countries and its association with cognitive changes over 3-years. This prospective study included 2157 community dwelling-seniors 70 years and older, followed for 3 years as part of DO-HEALTH, a randomized clinical trial. At baseline, participants completed a food frequency questionnaire and C-Reactive Protein (CRP) and interleukin-6 (IL-6) was measured. We used the Montreal Cognitive Assessment (MoCA) every year of the study. Based on reduced rank regression, we estimated a dietary pattern associated with CRP and IL-6. To evaluate the association between the EDP and cognitive changes over time, we used repeated measure linear regression models adjusting for age, total calories, BMI, study center, time, alcohol intake, education, physical activity, presence of depression symptoms, hypertension, diabetes or heart disease. The EDP was characterized by higher intakes of red and organ meat, refined grains, legumes, poultry and white fish, and lower intakes of coffee, tea, ginger, nuts and cheese. In multivariate adjusted models, participants with lowest adherence to the EDP (range -7.3 to -0.3) increased their MoCA scores 0.7 points over three years whereas those with highest adherence (range 0.4-10.1) increased their MoCA scores only by 0.2 points (p=0.01). In conclusion, a low inflammatory diet was associated with better cognitive function over time among adults ≥70 years from five European countries. This finding supports the role of diet in the promotion of cognitive health among 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".