Association between Diabetes and Cognitive Function among People over 45 Years Old in China: A Cross-Sectional Study
Bibliographic record
Abstract
Objectives: The aim of this study is to identify the relationship between diabetes status including characteristics of diabetes and cognition among the middle-aged and elderly population (≥45 years) in China. Methods: A sample of 8535 people who participated in the China Health and Retirement Longitudinal Study (CHARLS) from June 2011 to March 2012 was analyzed. Two cognitive domains including episodic memory and executive function were measured through questionnaires. People were classified into four groups: no diabetes, controlled diabetes, untreated diabetes, treated but uncontrolled diabetes. Weighted multiple regression model was conducted to explore the association between diabetes and cognition in full sample as well as three different age groups (45–59, 60–74, ≥75). Adjustments were made for demographics and cardiovascular risk factors. Results: After adjusting several covariates, untreated diabetes (β = −0.192, p < 0.05) was significantly associated with episodic memory. In the age group of 45–69 years, untreated diabetes (β = −0.471, p < 0.05) and HbA1c level (β = −0.074, p < 0.05) were significantly associated with episodic memory. When adjusting for cardiovascular risk factors, all correlations were non-significant. Conclusion: The cross-sectional study suggests that untreated diabetes and HbA1c are the potential risk factor for cognitive impairment, and these associations are more significant in the age group of 45–59 years old. Cardiovascular factors are important mediating factors in the pathway between diabetes and cognitive impairment. More longitudinal studies are needed to confirm these associations.
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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 | 0.001 | 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".