Cognitive Impairments In Type 2 Diabetes Mellitus: Clinical And Biochemical Correlations
Bibliographic record
Abstract
Background. Cognitive impairments are increasingly recognized as significant complications of Type 2 Diabetes Mellitus (T2DM), adversely affecting daily functioning, treatment adherence, and quality of life. Recent evidence suggests that metabolic dysregulation, endothelial dysfunction, and neuroinflammatory mechanisms contribute to the development of cognitive decline in diabetic patients. However, the interplay between biochemical markers such as glycated hemoglobin (HbA1c), adiponectin, and soluble vascular cell adhesion molecule-1 (sVCAM-1) and neuropsychological performance remains insufficiently explored. Objective. To evaluate the clinical and biochemical correlations of cognitive impairments in patients with Type 2 Diabetes Mellitus using standardized neuropsychological tests and serum biomarker levels. Methods. A cross-sectional analytical study was conducted at the Department of Neurology, Tashkent State Medical University from 2022 to 2024. The study included four groups: (1) T2DM with mild cognitive impairment; (2) T2DM with dementia; (3) T2DM without cognitive impairment; (4) healthy controls. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA), Frontal Assessment Battery (FAB), Stroop Test, and Hospital Anxiety and Depression Scale (HADS). Serum HbA1c, total adiponectin, and sVCAM-1 levels were measured using ELISA. Statistical analyses included correlation tests and regression modeling. Results. Patients with T2DM and cognitive impairment demonstrated significantly higher HbA1c and sVCAM-1 levels and lower adiponectin levels compared with both diabetic patients without cognitive impairment and healthy controls (p < 0.05). MoCA and FAB scores showed strong inverse correlations with HbA1c (r = –0.52, p < 0.01) and sVCAM-1 (r = –0.47, p < 0.01). Adiponectin demonstrated a positive correlation with cognitive scores (r = +0.44, p < 0.01). These findings support the concept of metabolic–vascular–neuroinflammatory interplay in diabetic cognitive dysfunction. Conclusion. Cognitive decline in T2DM is closely linked with biochemical alterations such as elevated HbA1c and sVCAM-1 and reduced adiponectin. These biomarkers may serve as early indicators of cognitive impairment and should be incorporated into comprehensive assessment strategies for diabetic patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".