Nε-(carboxymethyl)-lysine, White Matter, and Cognitive Function in Diabetes Patients
Bibliographic record
Abstract
OBJECTIVE: To study the relationship of Nε-(carboxymethyl)-lysine level (CML) with microstructure changes of white matter (WM), and cognitive impairment in patients with type 2 diabetes mellitus (T2DM) and to discuss the potential mechanism underlying T2DM-associated cognitive impairment. METHODS: The study was performed in T2DM patients (n=22) with disease course ≥5 years and age ranging from 65 to 75 years old. A control group consisted of 25 sex- and age-matched healthy volunteers. Fractional anisotropy (FA) of several WM regions was analyzed by diffusion tensor imaging scan. Plasma CML levels were measured by enzyme-linked immunosorbent assay, and cognitive function was assessed by Mini-Mental State Examination and Montreal cognitive assessment (MoCA). RESULTS: The total Mini-Mental State Examination score in the patient group (25.72±3.13) was significantly lower than the control group (28.16±2.45) (p<0.05). In addition, the total MoCA score in the patient group (22.15±3.56) was significantly lower than the control group 25.63±4.12) (p<0.01). In the patient group, FA values were significantly decreased in the corpus callosum, cingulate fasciculus, inferior fronto-occipital fasciculus, parietal WM, hippocampus, and temporal lobes relative to corresponding regions of healthy controls (p<0.05). Plasma CML level was negatively correlated with average FA values in the global brain (r=-0.58, p<0.01) and MoCA scores (r=-0.47, p<0.05). CONCLUSIONS: In T2DM, WM microstructure changes occur in older patients, and elevations in CML may play a role in the development of cognitive impairment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".