Does α-synuclein have a dual and opposing effect in preclinical vs. clinical Parkinson's disease?
Bibliographic record
Abstract
α-Synuclein gene (SNCA) multiplications cause familial parkinsonism and allele-length polymorphisms within the SNCA dinucleotide repeat REP1 increase the risk for developing Parkinson's disease (PD). Since SNCA multiplications increase SNCA expression, and REP1 genotypes that increase the risk of developing PD show increased SNCA expression in cell-culture systems, animal models, and human blood and brain, PD therapies seek to reduce SNCA expression. We conducted an observational study of 1098 PD cases to test the hypothesis that REP1 genotypes correlated with reduced SNCA expression are associated with better motor and cognitive outcomes. We evaluated the association of REP1 genotypes with survival free of Hoehn and Yahr stages 4 or 5 (motor outcome) and of Modified Telephone Interview for Cognitive Status score ≤27 or Alzheimer's Disease Dementia Screening Interview score ≥2 (cognitive outcome). Median disease duration at baseline was 3.3 years and median lag time from baseline to follow-up was 7.8 years. Paradoxically, REP1 genotypes associated with increased risk of developing PD and increased SNCA expression were associated with better motor (HR = 0.87, p = 0.046, covariate-adjusted age-scale analysis; HR = 0.85, p = 0.020, covariate-adjusted time-scale analysis) and cognitive outcomes (HR = 0.90, p = 0.12, covariate-adjusted age-scale analysis; HR = 0.85, p = 0.023, covariate-adjusted time-scale analysis). Our findings raise the possibility that SNCA has a dual, opposing, and time-dependent role. This may have implications for the development of therapies that target SNCA expression.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".