Prevalence, Influence Factors and Cognitive Characteristics of Mild Cognitive Impairment in Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Background Type 2 diabetes mellitus (T2DM) is considered as an independent risk factor for mild cognitive impairment (MCI). This study was performed to investigate the prevalence, influencing factors and cognitive characteristics of MCI in elderly patients with T2DM in China. Methods In the cross-sectional study, we performed cluster random sampling of 3246 people age 60 years and older across the country. All participants were interviewed and screened for T2DM and MCI. A total of 341 subjects were diagnosed of MCI according to the criteria of Petersen, and a total of 256 subjects were diagnosed of T2DM by using the American Diabetes association criteria Among the 256 T2DM people, 56 were also diagnosed with MCI. Logistic regression analyses were performed to evaluate risk and protective factor for MCI with T2DM. We also assessed their cognitive function by using the Mini-mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Digit span, Associative learning test, Visual identification test, Verbal fluency, Wechsler Adult Intelligence Scale (WAIS)-III Block Design, Wechsler Adult Intelligence Scale (WAIS)-III picture completion and Auditory verbal learning test. Results Among the 256 T2DM patients, 56 were diagnosed with MCI, and the prevalence of MCI in T2DM was 21.8%. Multivariate logistic regression analyses showed that depression (p=0.002, OR=6.220, 95%CI: 2.005~19.290) was a risk factor for MCI among T2DM patients, while education (p0.05) in neuropsychological tests between T2DM-MCI group and No-T2DM-MCI group. Linear regression analysis showed that the drug treatment of diabetes was positively correlated (t=2.263, p=0.025) with the total score of auditory word tests. Conclusions The present study suggests a high prevalence of MCI among Chinese T2DM patients. depression is a risk factors for MCI, while education is a protective one. T2DM patients with MCI often show comprehensive cognitive impairment, and the drug treatment of diabetes might help to improve cognitive function. Key words: T2DM, MCI, Chinese elderly, auditory verbal learning test
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".