SLC6A3 Polymorphism Predisposes to Dopamine Overdose in Parkinson's Disease
Bibliographic record
Abstract
In Parkinson’s disease (PD), cognitive functions mediated by brain regions innervated by the substantia nigra pars compacta (SNc) improve with dopamine replacement therapy, whereas, functions mediated by regions innervated by the ventral tegmental area (VTA) worsen. We investigated whether these effects are modulated by the presence of a common polymorphism (9R) in SLC6A3, the gene that encodes dopamine transporter (DAT). 9R carriers have higher DAT concentrations and subsequently lower baseline dopamine concentrations than wildtype individuals. We investigated the effect of SLC6A3 genotype on encoding and recall of abstract images using the Aggie Figures Learning Test in PD patients both on and off medication. Encoding depends upon brain regions innervated by the VTA, whereas recall depends upon brain regions innervated by the SNc. We found that dopaminergic therapy worsened encoding of abstract images in 9R carriers only. In contrast, dopaminergic therapy improved recall of abstract images in all PD patients, irrespective of SLC6A3 genotype. We found that 9R-carrier PD patients are predisposed to dopamine overdose and medication-induced impairment of cognitive functions mediated by VTA-innervated brain regions. Interestingly, PD patients without the 9R polymorphism did not show such an impairment. SLC6A3 genotype does not modulate the dopaminergic therapy-induced improvement of functions mediated by SNc-innervated regions in PD 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".