The association between macronutrient intake and cognition in individuals aged under 65 in China: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: The aim of this retrospective study was to explore the correlation between daily energy intake from macronutrients and cognitive functions in a Chinese population aged less than 65 years. DESIGN: test were used to compare the demographic and physical characteristics, lifestyle and laboratory parameters with the intake of macronutrients among different quartiles of % fat/energy. Multivariate logistic regression analysis was applied to identify the potential risk factors of mild cognitive impairment (MCI). PARTICIPANTS: Young and middle-aged participants (age <65 years) were recruited from Beijing, China. The Montreal cognitive assessment (MoCA) and mini-mental state examination (MMSE) were used to evaluate the cognitive functions, and the dietary intake of the participants was estimated with a semi-quantitative food frequency questionnaire (FFQ). RESULTS: Among the 661 participants, 80 (12.1%) had MCI, while 581 (87.9%) had normal cognitive functions. On evaluating the data based on the age group, educational background, and conditions of hyperlipidaemia and total energy intake, the results revealed that high % fat (upper quartile: adjusted OR (aOR) 3.90, 95% CI1.53 to 9.89, P=0.004), and high % protein intake (upper quartile: aOR 2.77, 95% CI 1.24 to 6.15) were greatly associated with increased frequency of MCI, while high % carbohydrate intake (upper quartile: aOR0.30, 95% CI 0.12 to 0.72) was correlated with decreased prevalence of MCI. CONCLUSION: The dietary pattern with high percentage of energy intake from fat and protein, and low-energy intake from carbohydrate might have been associated with cognitive decline in a Chinese population under 65 years of age.
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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 | 0.002 | 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".