Head injury, alpha‐synuclein Rep1, and Parkinson's disease
Bibliographic record
Abstract
OBJECTIVE: To test the hypothesis that variability in SNCA Rep1, a polymorphic dinucleotide microsatellite in the promoter region of the gene encoding α-synuclein, modifies the association between head injury and Parkinson's disease (PD) risk. METHODS: Participants in the Farming and Movement Evaluation (FAME) and the Study of Environmental Association and Risk of Parkinsonism using Case-Control Historical Interviews (SEARCH), 2 independent case-control studies, were genotyped for Rep1 and interviewed regarding head injuries with loss of consciousness or concussion prior to Parkinson's disease (PD) diagnosis. Logistic regression modeling adjusted for potential confounding variables and tested interaction between Rep1 genotype and head injury. RESULTS: Consistent with prior reports, relative to medium-length Rep1, short Rep1 genotype was associated with reduced PD risk (pooled odds ratio [OR], 0.7; 95% confidence interval [CI], 0.5-0.9), and long Rep1 with increased risk (pooled OR, 1.4; 95% CI, 0.95-2.2). Overall, head injury was not significantly associated with PD (pooled OR, 1.3; 95% CI, 0.9-1.8). However, head injury was strongly associated with PD in those with long Rep1 (FAME OR, 5.4; 95% CI, 1.5-19; SEARCH OR, 2.3; 95% CI, 0.6-9.2; pooled OR, 3.5; 95% CI 1.4-9.2, p-interaction = 0.02). Individuals with both head injury and long Rep1 were diagnosed 4.9 years earlier than those with neither risk factor (p = 0.03). INTERPRETATION: While head injury alone was not associated with PD risk, our data suggest head injury may initiate and/or accelerate neurodegeneration when levels of synuclein are high, as in those with Rep1 expansion. Given the high population frequency of head injury, independent verification of these results is essential.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".