Possible role of glial cell line-derived neurotrophic factor for predicting cognitive impairment in Parkinson’s disease: a case-control study
Bibliographic record
Abstract
Glial cell line-derived neurotrophic factor (GDNF) plays an important role in the protection of dopaminergic neurons, but there are few reports of the relationship between GDNF and its precursors (α-pro-GDNF and β-pro-GDNF) and cognitive impairment in Parkinson's disease. This study aimed to investigate the relationship between the serum levels of GDNF and its precursors and cognitive impairment in Parkinson's disease, and to assess their potential as a diagnostic marker. Fifty-three primary outpatients and hospitalized patients with Parkinson's disease (23 men and 30 women) with an average age of 66.58 years were enrolled from the Affiliated Hospital of Xuzhou Medical University of China in this case-control study. The patients were divided into the Parkinson's disease with cognitive impairment group (n = 27) and the Parkinson's disease with normal cognitive function group (n = 26) based on their Mini-Mental State Examination, Montreal Cognitive Assessment, and Clinical Dementia Rating scores. In addition, 26 age- and sex-matched healthy subjects were included as the healthy control group. Results demonstrated that serum GDNF levels were significantly higher in the Parkinson's disease with normal cognitive function group than in the other two groups. There were no significant differences in GDNF precursor levels among the three groups. Correlation analysis revealed that serum GDNF levels, GDNF/α-pro-GDNF ratios, and GDNF/β-pro-GDNF ratios were moderately or highly correlated with the Mini-Mental State Examination, Montreal Cognitive Assessment, and Clinical Dementia Rating scores. To explore the risk factors for cognitive impairment in patients with Parkinson's disease, logistic regression analysis and stepwise linear regression analysis were performed. Both GDNF levels and Hoehn-Yahr stage were risk factors for cognitive impairment in Parkinson's disease, and were the common influencing factors for cognitive scale scores. Neither α-pro-GDNF nor β-pro-GDNF was risk factors for cognitive impairment in Parkinson's disease. A receiver operating characteristic curve of GDNF was generated to predict cognitive function in Parkinson's disease (area under the curve = 0.859). This result indicates that the possibility that serum GDNF can correctly distinguish whether patients with Parkinson's disease have cognitive impairment is 0.859. Together, these results suggest that serum GDNF may be an effective diagnostic marker for cognitive impairment in Parkinson's disease. However, α-pro-GDNF and β-pro-GDNF are not useful for predicting cognitive impairment in this disease. This study was approved by Ethics Committee of the Affiliated Hospital of Xuzhou Medical University, China (approval No. XYFY2017-KL047-01) on November 30, 2017.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".