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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".