Polymorphisms of Catechol-O-Methyl Transferase (COMT) Gene in Vulnerability to Levodopa-Induced Dyskinesia
Bibliographic record
Abstract
PURPOSE: Parkinson's disease (PD), a common neurodegenerative disorder, is usually treated with Levodopa (L-DOPA). The use of this drug, however, is severely limited by the development of side effects of the motor system: Levodopa-induced dyskinesia (LID). The aim of this study is to investigate the association between seven COMT gene single-nucleotide polymorphisms (SNPs) and the development of LID in patients with PD. METHODS: 232 Caucasian patients with PD were investigated. 212 patients with PD received Levodopa therapy. Dyskinesia was assessed with the use of the Abnormal Involuntary Movement Scale (AIMS). Genotyping was carried out on seven SNPs of the COMT gene (rs4680, rs6269, rs4633, rs4818, rs769224, rs165774, rs174696) using a real-time PCR method, and blind to the clinical status of the subjects. RESULTS: We found association between four SNPs, rs165774, rs4818, rs4633, rs4680, and LID. When the duration of disease was added as a covariate in regression analysis, however, the results did not reach statistical significance. Only the additive model for rs165774 was found to be close to be statistical significance (OR = 1.627 [0.976-2.741], permutation p = 0.057). CONCLUSIONS: The results failed to clearly support a contribution of the studied polymorphisms; this may be related to a dominant relationship with the disease duration confounding the effect on the prevalence of LID.
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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".