Serum NPTX2 and cognitive impairment in geriatric diabetes
Bibliographic record
Abstract
Cognitive impairment is an increasingly common complication of diabetes, yet its underlying pathophysiological mechanisms remain poorly understood. Neuronal pentraxin 2 (NPTX2), a recently identified synaptic biomarker linked to cognitive disorders, has not previously been examined in relation to cognitive function in geriatric individuals with diabetes. This cross-sectional study enrolled 90 participants-46 geriatric patients with diabetes and 44 age-matched non-diabetic controls. Demographic and clinical data were collected for all participants. After informed consent, cognitive function was assessed with the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE). Serum NPTX2 levels were measured by ELISA. No significant differences were found between the diabetic and control groups in age, sex, education level, marital status, smoking history, comorbid conditions, or polypharmacy. However, the groups differed significantly in MoCA scores (p < 0.001), MMSE scores (p = 0.028), and NPTX2 levels (p = 0.048); the diabetic group showed lower cognitive scores and biomarker levels. NPTX2 levels correlated positively with MoCA and MMSE scores and negatively with diabetes duration, patient age, and the presence of microvascular complications. In conclusion, cognitive function was significantly lower in geriatric patients with diabetes than in controls, and serum NPTX2 levels were significantly associated with cognitive performance. These findings suggest a possible role for NPTX2 in diabetes-related cognitive decline and support further investigation of its utility within a broader biomarker panel.
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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 | 0.000 | 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".