The Role of Inflammation, Oxidative Stress, Neuronal Damage, and Endothelial Dysfunction in the Neuropathology of Cognitive Complications in Diabetes: A Moderation and Mediation Analysis
Bibliographic record
Abstract
OBJECTIVE: Cognitive impairment is increasingly recognized as a complication of diabetes, yet the underlying pathology remains unclear. This study aims to investigate the roles of inflammation, oxidative stress, endothelial dysfunction, and neuronal damage in the neuropathology underlying diabetes related cognitive impairment. METHODS: This study assessed 183 participants (54 prediabetes, 71 Type 2 diabetes mellitus [T2DM], and 58 controls) for cognitive performance using the Montreal Cognitive Assessment (MoCA). Blood samples were analyzed for interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), malondialdehyde (MDA), VCAM-1/CD106, and neuron-specific enolase (NSE) using ELISA. Mediation and moderator analysis methods were used to examine the roles of these biomarkers in diabetes-related cognitive impairment. RESULTS: After adjusting for age, education, and gender, group comparisons revealed significant cognitive impairment in patients with T2DM, particularly in visuospatial functions, naming, language, and memory performance, compared to the control group. The patients with T2DM and prediabetes exhibited similar performance in cognitive functions, except for language. Significant differences in VCAM-1 and TNF-α levels were observed; however, these biomarkers did not mediate the effect of T2DM and prediabetes on cognitive functions. Nevertheless, VCAM-1 was found to moderate abstraction abilities in T2DM. CONCLUSION: Prediabetes represents a transitional stage not only for the pathology of diabetes but also for cognitive complications. Although there were correlations between cognitive performance and various cognitive scores, IL-6, MDA, NSE, VCAM-1, and TNF-α did not play a mediator role in the neuropathology of diabetes-related cognitive impairment.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".