Dietary patterns, inflammatory biomarkers and cognition in older adults: An analysis of three population-based cohorts
Bibliographic record
Abstract
BACKGROUND: Targeting effective strategies to prevent cognitive decline is key in the aging population. Some diets have been linked to a slower cognitive decline, potentially through reducing inflammation. We aimed at determining the effect of inflammatory dietary patterns (IDPs) on cognitive function in three population-based cohorts. METHODS: In this longitudinal study, we analyzed data from the Canadian Longitudinal Study of Aging, CoLaus|PsyCoLaus and Rotterdam Study. Our analytical sample included participants over 55 years old with baseline data on cognition, dietary intake, and inflammatory markers. IDPs were derived for each cohort using reduced rank regression to reflect maximal variation in three inflammatory markers. We calculated scores of consumption of the IDPs, higher scores indicating more IDP consumption. We used inverse probability of treatment and censoring weights in the marginal structural models to estimate associations of higher versus lower quarters of consumption of an IDP on general cognition (Mini-Mental State Evaluation) and four cognitive domains (memory, verbal fluency, verbal learning and processing speed and executive function) during at least 3 years of follow-up. RESULTS: We included 10,366 participants (mean age 68) followed-up for a mean of 5 years. Diet explained between 1 and 2% of the variation of the inflammatory markers. There were no differences in general cognition when comparing the highest to the lowest quarter of consumption of IDPs among the three cohorts. Mean differences for the four cognitive domains were of small magnitude across cohorts and not clinically relevant. CONCLUSION: Diet explained low variation in inflammatory markers. Consuming IDPs was not associated with mean differences in general or domain-specific cognitive function.
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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".