Cognitive domain-specific impairments and associated risk factors in type 2 diabetes mellitus: a cross-sectional observational study based on neuropsychological assessment from Xiamen, China
Bibliographic record
Abstract
Background Type 2 diabetes mellitus (T2DM) is associated with an increased risk of cognitive impairment, yet limited research has been conducted in subtropical regions of China. Objective To examine the characteristics of cognitive impairment and identify the potential risk factors in patients with T2DM in Xiamen. Methods This cross-sectional observational study included 84 patients with T2DM from Zhongshan Hospital Xiamen University. Patients were grouped based on their Montreal Cognitive Assessment (MoCA) scores into a cognitively impaired group (T2DM-CI group, n = 52) and a cognitively normal group (T2DM-NCI group, n = 32). Multivariate logistic regression was used to identify independent risk factors. Results Among the 52 patients in the T2DM-CI group, the most commonly affected cognitive domains were executive function (82.7%), language (75.0%), memory (61.5%), and attention (48.1%), with 59.6% exhibiting impairments in three or more domains. Compared with the T2DM-NCI group, the T2DM-CI group showed poorer performance in most MoCA subdomains—including visuospatial/executive function, language, delayed recall, abstraction, and orientation—as well as in individual cognitive domain tests (all P < 0.05), except for the Clock Drawing Test. Older age (OR = 1.167, 95% CI [1.045–1.303], P = 0.006) and higher lipoprotein (a) levels (OR = 1.109, 95% CI [1.020–1.205], P = 0.015) were independently associated with cognitive impairment in T2DM patients. Conclusion Cognitive impairment in T2DM affects multiple domains, with executive dysfunction most prominent. Age and elevated lipoprotein(a) may increase risk. Routine cognitive screening is warranted, particularly in older patients and those with vascular risk factors.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| 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 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".