Different risk factors for cognitive impairment among community-dwelling elderly, with impaired fasting glucose or diabetes
Bibliographic record
Abstract
Aim: The aim of this study was to investigate whether elderly people with impaired fasting glucose (IFG) or diabetes mellitus (DM) share the common risk factors for cognitive impairment as compared to normal blood glucose population. Methods: This cross-sectional study assessed 10,039 community-dwelling participants aged ≥ 55 years in Beijing, China. According to the glycemic status, subjects were classified into three groups: normal fasting plasma glucose (NG, n=6399), impaired fasting glucose (IFG, n=873) and DM (n=2626). The Mini-Mental State Examination (MMSE) was applied to evaluate the cognitive function status of the study population. Potential demographic, clinical, and genetic risk factors for cognitive impairment were collected and compared across the three groups. Multivariate logistic regression model was performed to explore the risk factors associated with cognitive impairment. Results: Education-modified MMSE scores in the participants with NG, IFG, and DM were 26.91±3.94, 26.67±4.00, and 26.58±4.11, respectively ( P =0.0008). In the age- and sex-adjusted comparisons, the MMSE scores in subjects with DM and IFG were significantly lower than that in subjects with normal glucose ( P =0.01 and P =0.02, respectively). The logistic regression analysis showed that risk factors only in the NG population were older age, female, apoEε4 carrier, normal or lower uric acid (UA) levels. Hypertension was an independent risk factor only in IFG group, and the history of stroke and depression were the risk factors associated with cognitive impairment only in the DM group. Conclusion: Subjects with DM or IFG had a lower performance on the MMSE test compared with subjects who had normal blood glucose. The elderly with diabetes and IFG have some different risk factors for cognitive impairment as compared to those with normal blood glucose. Keywords: cognition, pre-diabetes, dementia, cognitive decline, Mini-Mental State Examination
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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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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".