Association between prediabetes and cognitive function in Parkinson's disease
Bibliographic record
Abstract
INTRODUCTION: It remains largely unknown whether prediabetes is related to cognitive impairment in Parkinson's disease (PD). This study aimed to assess the association between prediabetes and cognitive function in PD patients. METHODS: In this cross-sectional study, 262 PD patients (age, 69.8 ± 10.3 years; Hoehn-Yahr stage, 2.3 ± 0.8) were classified into diabetes (glycated hemoglobin [HbA1c] ≥6.5% or previously diagnosed, n = 76), prediabetes (5.7%-6.4%, n = 90), or diabetes free (≤5.6%, n = 96) groups. Cognitive function was measured using the Montreal Cognitive Assessment (MoCA) test. RESULTS: Both the diabetes and prediabetes groups had significantly lower MoCA scores (17.0 ± 6.6 and 18.0 ± 6.1, respectively) than the diabetes free group (20.0 ± 5.7), even after adjusting for potential confounders (p = .002 and p = .008, respectively). In the combined group of prediabetes and diabetes free patients, higher HbA1c levels significantly correlated with lower MoCA scores (p = .031). There was a significant interaction of diabetes status with age, but not with the duration of PD, on cognitive function. CONCLUSION: In addition to diabetes, prediabetes may negatively affect cognitive function in PD patients. Further prospective longitudinal studies are necessary to clarify the impact of prediabetes on the cognitive trajectory of these 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 | 0.001 |
| 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".