Higher versus lower nut consumption and changes in cognitive performance over two years in a population at risk of cognitive decline: a cohort study
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Tree nuts and peanuts (henceforth, nuts) are nutrient-dense foods rich in neuroprotective components; thus, their consumption could benefit cognitive health. However, evidence to date is limited and inconsistent regarding the potential benefits of nuts for cognitive function. OBJECTIVE: To prospectively evaluate the association between nut consumption and 2-y changes in cognitive performance in older adults at cognitive decline risk. METHODS: A total of 6,630 participants aged 55 to 75 y (mean age 65.0±4.9 y, 48.4% women) with overweight/obesity and metabolic syndrome completed a validated semi-quantitative food frequency questionnaire and a comprehensive battery of neuropsychological tests at baseline and a 2-y follow-up. Composite cognitive scores were used to assess global, general, attention, and executive function domains. Nut consumption was categorized as <1, ≥1 to <3, ≥3 to <7, and ≥7 servings/wk (1 serving=30 g). Multivariable-adjusted linear regression models were fitted to assess associations between baseline nut consumption and 2-y cognitive changes. RESULTS: Nut consumption was positively associated with 2-y changes in general cognitive function (P-trend <0.001). Compared with participants consuming <1 serving/wk of nuts, those categorized as consuming ≥3 to <7 and ≥7 servings/wk showed more favorable changes in general cognitive performance (β z-score [95% CI] = 0.06 [0.00,0.12] and 0.13 [0.06,0.20], respectively). No significant changes were observed in the multivariable-adjusted models for other cognitive domains assessed. CONCLUSION: Frequent nut consumption was associated with a smaller decline in general cognitive performance over 2 y in older adults at risk of cognitive decline. Randomized clinical trials to verify our findings are warranted.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it