Glucose Metabolism and Cognitive Decline in Progressive Supranuclear Palsy and Corticobasal Syndrome: A Preliminary Study
Bibliographic record
Abstract
Multiple studies have analyzed the possible correlations between diabetes and Alzheimer's disease. Less is known about the context of cognitive deterioration among patients with atypical Parkinsonian syndromes and glucose metabolism impairment. The aim of this study was to evaluate the association between the impaired glucose metabolism and cognitive decline among patients with progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS). The study included 22 patients with PSP and CBS with disease durations varying from 3 to 6 years. The levels of glycated hemoglobin (HbA1C), fasting blood glucose, fasting C-peptide and the presence of microalbuminuria were evaluated, and oral glucose tolerance tests (OGTT) were performed. Based on the OGTT results, the glycemic variability, mean glycemia, glycemia standard deviation (SD) and coefficient of variation (%CV) were calculated. All patients underwent a three-Tesla brain magnetic resonance (MRI) examination and neuropsychological cognitive assessment with the use of standardized scales: Montreal Cognitive Assessment (MoCA), Mini-Mental State Examination (MMSE) and Frontal Assessment Battery (FAB). A statistical analysis revealed that poor control of glycemia with high glycemic variability and increased atrophy of the medial temporal lobe among patients with PSP and CBS correlated with worse cognitive performance independent of age or sex, even among patients who did not fulfill the criteria for diabetes. The study results indicate the importance of glucose metabolism control and optimal treatment in the context of cognition maintenance among patients with PSP and CBS. Due to the relatively small number of analyzed patients, the issue requires further assessment. To the best of our knowledge, this is the first study discussing the role of glycemic variability in atypical Parkinsonian syndromes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".