Parkinson Disease <i>SNCA</i> Risk Variants Are Associated With Higher Asymmetric Putamen Dopaminergic Dysfunction
Bibliographic record
Abstract
Objectives The aim of this study was to investigate the endophenotypic potential of striatal dopamine transporter (DAT) uptake in carriers of Parkinson disease (PD)–associated SNCA genetic risk variants. Methods We analyzed 381 patients with de novo PD from the Parkinson's Progression Markers Initiative (PPMI). The genotype of previously identified PD-related SNCA risk variants was extracted and used to compute an individual PD - specific SNCA genetic risk score (GRS). Striatal DAT uptake was quantified using 123 I‐FP‐CIT SPECT and assessed at baseline and 24-month follow-up. Mixed models were applied to explore the relationship between striatal 123 I‐FP‐CIT SPECT specific binding ratios (SBRs) and PD SNCA risk variants. Results No significant associations were observed between SNCA risk variants and the mean putamen or caudate 123 I‐FP‐CIT SPECT SBRs. However, a higher SNCA GRS was significantly associated with increased baseline putamen 123 I‐FP‐CIT SPECT SBR asymmetry index ( p < 0.001). This relationship appeared primarily driven by the 3′ variant rs356182 and the 5′ region variant rs763443 ( p < 0.001) and was not observed at 24-month follow-up. Discussion Our findings suggest that a more lateralized putaminal dopaminergic degeneration in early PD may represent a viable endophenotype in carriers of SNCA risk variants, emphasizing the relevant role of neuroimaging in PD subtyping and biomarker identification.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".